01. PhD Thesis

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    2D/3D NOVEL MATERIALS FOR HIGH PERFORMANCE PEROVSKITE SOLAR CELLS
    (Nazarbayev University School of Engineering and Digital Sciences, 2022-04) Aidarkhanov, Damir
    The continuous increase of energy demand and emission of greenhouse gases from the conventional fossil fuels signifies the importance of renewable energy. The solar radiation is a readily available renewable energy source. If the amount of solar energy irradiated on the earth can be converted into electrical energy very efficiently, the energy demand of our daily life can be satisfied. The photovoltaics (i.e. solar cells) are the devices directly converting the solar irradiation into the electrical energy. Among the existing photovoltaic technologies, the metal halide perovskite solar cells (PSCs) demonstrate a huge potential of realizing cost-effective and high-performance devices for future practical applications. The theoretical calculations demonstrate that a single junction PSC can reach a power conversion efficiency (PCE) above 30%. However, there are still a number of challenges hindering the commercialization of PSCs for the practical use. This work focuses on enhancing the performance of PSCs via application of novel 2D/3D materials and engineering of device architectures. A multilayer structure for electron-transporting layer (ETL) has been developed for high performance PSCs. It is shown that a triple-layer ETL consisted of SnO2 quantum dots, SnO2 nanoparticles, and fullerene-derivative based passivation layer can facilitate the carrier transports due to optimization of surface morphology of ETL which yields a better interface quality for subsequently deposited perovskite absorber layer. The defect states residing the interface between the ETL and perovskite are also reduced by optimizing the architecture of ETL in PSCs. Further, a two-dimensional material, black phosphorus (BP) in form of nanoflakes was used to modify the interface between the ETL and the perovskite layer. The application of BP in PSCs demonstrates an increase of the device efficiency and stability. The positive effect introduced by BP is attributed to the improved perovskite crystallization on BP modified ETL and passivation of interfacial defects by lone-pair electrons of BP. Meanwhile, the photovoltaic properties of multiple cations mixed-halide perovskite layer can be improved by incorporation of a cross-linking material, 2,2′-(Ethylenedioxy) bis(ethylammonium iodide). The PSCs incorporated with an optimized concentration of cross-linking material demonstrate an enhancement of PCE and improvement in stability, which are attributed to the passivation of the defect states located at the surface and grain boundaries of perovskite by the cross-linking molecules. The cross linker assisted crystallization also leads to the formation of compact perovskite thin films, which could suppress the penetration of various species such as moisture, oxygen etc. from the atmosphere
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    3D CFD-DEM-IBM SIMULATIONS OF SAND PRODUCTION IN OIL WELLS
    (Nazarbayev University School of Engineering and Digital Sciences, 2021-09-16) Rakhimzhanova, Aigerim
    Sand production is particularily prominent in sandstone reservoirs, which are common to observe in the majority of oil and gas fields. When sand particles start to erode from weak sandstone formations for different reasons, their impact could lead to the decline of the production flow rate and equipment degradation, which will results in a huge economical loss. In some cases, it results in the end of production life of a well and reservoir. The key to overcome this problem and achieve accurate prediction of sand production may lie in the understanding of the cause of sanding mechanism. The current numerical approaches to predict the sanding mechanism are based on continuum and non-continuum models. The majority of developed models are based on the continuum approach, while a few discontinuum-based (DEM – Discrete Element Method) have been developed in the last two decades. Sand production is a dynamic and continuous process, which starts from microscopic scales where the rock is discontinuous in nature. It is impossible to capture local discontinuous phenomena using continuum-based models. The DEM models can capture the interaction and motion of each sand grain, the failure micro mechanism in a dynamic process at micro and macro scales, which makes it possible to simulate the sanding phenomena. In this research the DEM is firstly used for the rock characterization, where a simple 3D bond contact model for cemented sandstone material is developed by modifying the previous existing JKR (Johnson-Kendall-Roberts) model for auto-adhesive silt size sand particles, and the model parameter is the bond strength in terms of the interface energy. The material properties of the synthetic sandstone specimens equivalent to the Ustyurt-Buzachi Sedimentary Basin core samples were reproduced for the numerical specimens and the triaxial compression test results show that the numerically simulated macroscopic response is in good agreement with the experimental results of the cemented sandstone. The main aim of this research is to develop the sample preparation procedure/method with physical perforation penetration and sand production modelling in a periodic cell and by developing and using the combined 3D CFD-DEM-IBM modelling techniques (CFD – Computational Fluid Dynamics; IBM – Immersed Boundary Method). The application of the IBM is proposed to simulate the complex interaction between the geometry of the cased horizontal well completion opening and the weakly cemented sandstone under the overburden pressure and drawdown. The capability of developed methods to capture sand arching, damage zone (due to the perforation penetration) and sanding mechanism (erosion near the perforation hole) due to the pressure drawdown are presented. This study shows the mechanism of sand production in a bottom-up approach in the first 0.1 sec of sanding initiation immediately after the perforation penetration in oil wells, which will help engineers to better understand the sanding mechanism at the micro levels and how the problem of sanding can eventually be overcome though better insight into the phenomenon.
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    AIRBORNE PARTICULATE MATTER IN ASTANA, KAZAKHSTAN: POTENTIALLY TOXIC ELEMENTS, LUNG BIOACCESSIBILITY, AND RISK ASSESSMENT
    (Nazarbayev University, School of Engineering and Digital Sciences, 2024-04-26) Agibayeva, Akmaral
    The degradation of air quality remains one of the most critical environmental concerns. Exposure to airborne pollutants is extensively associated with various health conditions, including respiratory and cardiovascular diseases, and premature death. The health risks of air pollution have been linked to particulate matter (PM) and its constituents. Potentially Toxic Elements (PTEs) in atmospheric PM are a critical factor contributing to its toxicity. This doctoral thesis addresses multiple aspects of air quality in Astana, Kazakhstan, offering a holistic understanding of the local air pollution situation through (1) analysis of PM and gaseous pollutant concentration; (2) proposing a modification to the toxicity assessment of PM-bound PTEs via in vitro lung bioaccessibility; (3) the assessment of health risk due to inhalation exposure to PM using bioaccessible concentration of PTEs; (4) morphological characterization of PM; (5) source identification; (6) studying precipitation chemistry and its role in air pollution; and (7) assessment of the public knowledge, perception and attitude towards local air quality in Astana. The methodological framework involved primary data analysis (342 PM samples collected in Astana, Kazakhstan from 2021 to 2023) and air pollution data obtained from monitoring stations located in the city (S1-S6) in 2018-2020. Annual and 24-hour mean concentrations of PM2.5, PM2.5-10, and gaseous pollutants (SO2, CO, NO2, NO, and HF) were, in general, higher than established national and international (World Health Organization (WHO)) maximum permissible levels (e.g., for PM2.5 annual mean of 29.7 μg/m3 in 2018-2019; and 24-hour mean of 28.7 μg/m3 (maximum: 534 μg/m3) for PM2.5 and 226 μg/m3 (maximum: 1,564 μg/m3) for PM2.5-10, respectively, in 2021-2023). To simulate real-life inhalation exposure to PM-bound PTEs, the assessment was conducted through optimization of in vitro lung bioaccessibility testing in simulated lung fluids (SLF) (i.e., modified Gamble’s solution (GS) and Artificial Lysosomal Fluid (ALF)). For a modification of commonly established methodology, a large set of PTEs (Cd, Co, Cr, Cu, Mn, Ni, Pb, Sb, V, and Zn) has been investigated using seven distinct formulations of GS, one ALF on two reference materials (SRM 2691 and BGS 102). The bioaccessibility of the selected PTEs generally increased in modified GS with the incorporation of 5% DPPC (phospholipid) (e.g., from 2.87% to 8.35% for V in BGS 102), 0.25% cholesterol (e.g., from 27.3% to 31.5% for Cr in SRM 2691), and 5% DPPC + 0.5% cholesterol (e.g., from 43.5% to 51.5% for Cu in BGS 102). Therefore, using DPPC + cholesterol may be recommended for routine bioaccessibility testing. The effect of the tested solid-to-liquid ratio (S/L) was sample and element-specific. Overall, a lower S/L led to a higher bioaccessibility % in ALF. For all PTEs, the peak bioaccessibility was reached at a 4-week extraction, suggesting a longer testing duration when feasible. The optimized parameters for in vitro bioaccessibility were later applied for inhalation bioaccessibility of selected PTEs (i.e., Cd, Co, Cr, Cu, Fe, Mn, Ni, Pb, V, and Zn) in PM2.5 collected in Astana, Kazakhstan. The highest bioaccessible concentration was observed for Fe (mean: 16,229 mg/kg, range: (906-30,419 mg/kg) and V (mean: 10,725 mg/kg, range: (687-27,092 mg/kg). The inhalation Health Risk Assessment (HRA) using a bioaccessible concentration of PTEs in PM2.5 revealed acceptable carcinogenic and non-carcinogenic risks for adult and children exposure, although the maximum Cancer Rate (CR) for adults was slightly higher (1.01 × 10-6) than the established United States Environmental Protection Agency (U.S. EPA) threshold (HIc > 1 × 10-6). Scanning Electron Microscopy (SEM) analysis determined several major PM particle groups, including bioaerosols, coal fly ash (CFA), dust (natural or construction), and soot particles. Irregularly shaped, small-sized particles of CFA are associated with respiratory conditions and neurodevelopmental disorders, while soot particles of complex shapes can penetrate deeply into the respiratory system. In precipitation analysis, the mean concentration of major ions (i.e., F-, Cl-, NO2-, NO3-, SO42-, PO43-, K+, Na+, NH4+, Ca2+, Mg2+) remained within permissible levels for groundwater, drinking, and surface water. However, in April, the highest F- concentration (1.82 mg/L) exceeded the WHO limit for drinking water (1.5 mg/L). The concentration of most heavy metals (i.e., Cd, Co, Cr, Cu, Mn, Pb) was below WHO's maximum permissible levels, except for V, which exhibited the highest average concentration of 108 µg/L in precipitation samples across four seasons. The chemical analysis of PM and precipitation revealed common sources, including coal/liquid fuel combustion and vehicular exhaust. PM2.5 concentration modeling via Multiple Linear Regression (MLR) and Machine Learning (ML) Random Forest (RF) algorithms revealed PM10 and CO as major predictors of PM2.5 concentration. A real-life pollution scenario using Conditional Bivariate Probability Function (CBPF) analysis also suggested a substantial contribution of coal-heated power plant activity (CHPPs) and coal combustion from residential heating, coupled with emissions from internal combustion engine vehicles. Structural equation modeling (SEqM) was employed to investigate the causal relationship between perceived air quality, environmental literacy, and willingness to pay (WTP) for environmental protection. The age, education, and health status of the participants significantly affected (p < 0.001) their level of environmental knowledge and awareness. The SEqM analysis indicates that knowledge is the major determinant in improving public awareness and perception of local air pollution (path value = 0.626). The findings of the current research work can assist healthcare professionals and environmental researchers in public health-related decision-making and establishing feasible air quality guidelines.
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    APPLICATION OF PROBABILISTIC METHODS FOR EFFECTIVE AND RELIABLE OPERATION OF ELECTRICAL AND ELECTROMECHANICAL SYSTEMS
    (Nazarbayev University School of Engineering and Digital Sciences, 2021-06) Bapin, Yerzhigit
    This PhD thesis presents novel system control methods that can be utilized for effective and reliable operation of electric grids and passenger elevators. First of all, this study introduces a new spinning reserve allocation optimization technique that takes into account load and renewable power generation, inter-zonal conventional power generating capacity and demand response. Using the bivariate Farlie-Gumbel-Morgenstern probability density function, the framework presented in this thesis utilizes a new method to simulate the power generation of wind farms. In addition, the presented framework uses a Bayesian Network (BN) algorithm to fine-tune the spinning reserve allocation based on previous hours' actual unit commitment, as well as the hour and day types. The model proposed in this study has been tested on the IEEE Two-Area Reliability Test System (RTS) to quantify the effect of the bivariate wind prediction model and the Bayesian network-based Reserve Allocation Adjustment Algorithm (RAAA) on reliability and cost-effectiveness of the power grid. The findings show that combining a bivariate wind forecast model with RAAA improves power grid stability by 2.66 percent while lowering overall system running costs by 1.12 percent. Secondly, the present work introduces an algorithm with an objective of optimal dispatching control of passenger lifts. The algorithm utilizes the data received from video cameras and dispatches the elevator cars based on the passenger count. The proposed algorithm utilizes the information on the number of people and dispatches the lifts with an objective to move the maximum number of passengers to the desired building levels within the minimum amount of time. In addition, the algorithm considers each person's size and whether or not they have luggage. To account for uncertainty in image acquisition, the algorithm assigns the probability weights to the number of people who are waiting for a lift and riding the lifts. The main purpose of the algorithm is to minimize the following performance metrics: average travel time (ATT), average journey time (AJT) and average waiting time (AWT). The suggested algorithm works well in situations of limited traffic sizes, according to a test case scenario conducted on a ten-story office building having four elevator cars (less than 200 people). In a scenario with large up-peak high intensity traffic, the proposed algorithm primarily underperforms. The proposed algorithm's best output was seen in situations with random inter-floor passenger movement. In scenarios of changing traffic intensity and size ATT increased by 39.94 percent and 19.53 percent, respectively.
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    ASSESSING PHYTOPLANKTON COMMUNITIES IN MESOCOSMS AND ENDORHEIC KAZAKHSTANI LAKES USING IMAGING FLOW CYTOMETRY
    (Nazarbayev University School of Engineering and Digital Sciences, 2023-10-12) Dashkova, Veronika
    Accelerating climate change strongly affects inland aquatic ecosystems worldwide in physical, chemical, and biological aspects. Phytoplankton communities are the main producers, the basis of aquatic trophic webs, and are widely used as proxies for the evaluation of lake ecosystem states. They quickly react to environmental changes by shifting their composition, biomass, size, and biodiversity. However, the technologies, including light microscopy traditionally used for studying phytoplankton communities, may be challenging for research in multi-lake systems and complex mesocosm experiments requiring the processing of a large number of samples. Instead, high-throughput technologies such as imaging flow cytometry (IFC), combining flow cytometry and microscopy features and allowing a collection of large datasets of images and numerical parameters of plankton cells, provide a valuable alternative. The main goal of this study was to advance an IFC-based method for application to the routine analysis of phytoplankton community parameters in experimental artificial mesocosms and natural Kazakhstani lakes. Specific objectives include: 1. Advancement of methodology for phytoplankton, analysis based on IFC. 2. Application of advanced methodology to assess phytoplankton biomass, size distribution, and diversity in shallow lake mesocosms in response to nitrogen and temperature variations. 3. Application of advanced methodology to assess phytoplankton biomass, size distribution, and diversity along a salinity gradient in endorheic lakes, including Burabay National Nature Park (BNNP) lakes and the Aral Sea remnant waterbodies. A high-throughput IFC-based analysis pipeline capable of routine evaluation of hundreds of samples was advanced and the advanced approach was applied to characterize the phytoplankton community changes both in experimental artificial mesocosms and two natural systems of Kazakhstani lakes. Moreover, biovolume calculation formulae from microscopy 3D image analysis were adapted and modified to calculate cell biovolumes based on two-dimensional images obtained by IFC. Modified biovolume calculations were applied to the water samples from the mesocosms and endorheic lakes to obtain statistically significant quantitative data and enabled to explore patterns in phytoplankton communities in relation to the multiple environmental factors such as temperature, nutrients, and salinity gradient. In mesocosms, both nitrogen (N) and temperature variations significantly affected phytoplankton community structure. The addition of N in the phosphorus (P)-rich system caused the shift of dominance from colonial cyanobacteria to the dominance of chlorophytes and cyanobacteria. Phytoplankton size at varying temperature regimes was differentially affected by N enrichment: cellular size was reduced in the tanks with the highest temperature and increased at ambient and medium temperature tanks. The least phytoplankton diversity was caused by N enrichment at high temperatures. Phytoplankton community structure had significantly shifted along the salinity gradient in both former Aral Sea remnant water bodies and BNNP lakes. The community composition significantly differed between brackish, saline, and hypersaline sites. Also, using IFC analysis, we found that larger phytoplankton cells attributed to the brackish waters rather than saline and hypersaline sites. However, no distinct trends in phytoplankton diversity were observed in relation to the salinity gradient. We conclude that IFC methodology can be successfully used as a routine approach in both – quantitative and qualitative analysis of phytoplankton in artificial and natural water ecosystems.
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    AUGMENTED REALITY-BASED HUMAN MEMORY ENHANCEMENT USING ARTIFICIAL INTELLIGENCE
    (Nazarbayev University, School of Engineering and Digital Sciences, 2023-11-23) Makhataeva, Zhanat
    This thesis presents a novel approach to augmenting human memory using emerging technologies of augmented reality (AR), computer vision (CV), and artificial intelligence (AI). The newly proposed system creates an external representation of object location memory for indoor environments (i.e., objects placed on the three floors of the building) to replace internal mental representations created in the human mind. The system has two main components - a wearable module (i.e., an AR headset) and a computing module (i.e., a laptop computer with an Ubuntu Operating System). I designed a first-person view (FPV) application running on the wearable module of the system to sense the environment, send the sensed data to the computing module of the system for processing, and then receive the processed data about the surrounding environment from the AI module of the system. Based on the received data, the application generates and updates the external representation of the objects in the environment and the user path and integrates the digital representation into the real environment as the three-dimensional (3D) virtual object in front of the user’s eyes. For understanding the user and object locations, the computing module of the system uses a CV-based camera technology localization framework indoors and a real-time pre-trained object detector. Based on the user and object position data, the AI module of the system performs the object-to-location binding. Then, it sends this data to the AR headset to construct the external synthetic representation of the objects placed in the indoor environment of the multi-storey building. To explore the usability of the proposed system, I designed an experimental study and invited 26 participants (i.e., 12 females and 14 males) from the community of Nazarbayev University (NU) to complete the experimental task. In the study, participants were involved in two activities that required spatial memory skills, such as memorization of the location of objects in the building and pointing on the 2D map of the building the location of the object that they learned after the previous memorization process. In the first activity, participants walked along the three floors of the building and tried to memorize the locations and labels of the objects placed along the corridors. In the second activity, participants were asked to point on the two-dimensional (2D) plans of the three floors of the building the locations of memorized objects from the first activity. Participants completed the experimental task two times during the study. There were two sessions in the study. Participants were divided into two groups. The first group of participants completed the experimental task with the assistance of the AR system during the first session and without the assistance of the system during the second session. The second group of participants completed two sessions of experiments in the reversed order, at the beginning without AR assistance and then later with the assistance of the AR system. During the study, I tried to evaluate the usability of the system. Also, I compared the cognitive load experienced by participants when they completed the experimental task under two conditions (i.e., with and without the assistance of the AR system). In addition, I studied performance variables such as error rate and task completion time during the map-pointing activity. The results of the experimental study conducted with the participation of 26 people from the research and faculty community of NU revealed that the proposed human memory augmentation system helped to reduce the cognitive load of people during tasks that required memorization of object locations in the indoor environment and map-pointing on the 2D map of the environment the locations of learned objects. The results recorded during the map-pointing activity showed that participants made 7.52 times fewer errors and spent less time on the computer-based test when they performed the activity with the assistance of the AR system. After completing the experimental task with the assistance of the AR system, participants were asked to fill in the System Usability Score (SUS) questionnaire. The results showed that participants rated the system usability with an average score of more than 80% for both activities. I used statistical tests to evaluate the significance of the reported results of the behavioral study. In addition, I investigated whether the gender of the participants affected the results of the map-pointing activity. In this regard, the statistical test results applied to the recordings of the error rate and task completion time in map-pointing activity revealed no significant difference between the results of the male and female participants in the study. Also, I performed correlation analysis to study the dependence between pairs of variables such as different dimensions of the mental workload (i.e., mental demand, effort, temporal demand, frustration, physical demand, and performance), quantitative performance variables (i.e., error rate and task completion time), and usability. Correlation analysis showed a weak correlation between the usability recording and the mental workload. Results of the behavioral study indicated the potential of AR and AI-enhanced systems to help people during cognitive tasks that require human object location memory. This thesis highlights that combined capabilities of AR and AI technologies may result in the development of a new generation of smart technology-based solutions to support people’s cognition through augmented visualizations and sensor-based perception of the environment.
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    BLACK HOLE/MOVING MIRROR CORRESPONDENCE IN (1+1)-DIMENSIONS
    (Nazarbayev University, School of Engineering and Digital Sciences, 2023-11-10) Myrzakul, Aizhan
    The Hawking effect predicts that black holes can emit particles and energy when quantum mechanical effects are taken into account in quantum atmosphere around the black hole. However, certain models of black holes emit infinite energy and infinite particles that are contradictory to both classical and quantum theories' laws. These and other black hole evaporation problems along with the need to get experimental verification have underscored the need for analog and toy models that can solve the issues without losing the essential physical properties of the black hole radiation processes. The significance of studying moving mirrors is that they are accelerated boundaries that create energy, particles, and entropy similar to black holes. In fact, moving mirrors, which are simplified (1+1)-dimensional versions of the dynamical Casimir effect, act as toy models for black hole evaporation, in some cases, with an exact correspondence to the amount of particle production. Moreover, the dynamical Casimir effect has been measured in the laboratory within the framework of moving mirror model providing experimental observations and insight into the effect, whereas Hawking radiation from black holes effectively can not be measured because the effect is too small. The general and physically relevant connections of moving mirrors to black hole physics is a prime focus of this thesis. Here black holes and some cosmological models are approximated by (1+1)-dimensional moving mirrors. The detailed and complete investigation of all existing moving mirror models, their classifications and specific characters are the main objectives. This extensive study allows one to distinguish the moving mirror solutions that most physically describe black hole evaporation. They have proven capability to solve specific issues related to Hawking radiation. A new model related to the Schwarzschild black hole that solves the issue of finite energy with respect to Hawking radiation is developed. Also, it is established that Callan-Giddings-Harvey-Strominger (CGHS) black hole model has a correspondence to the exponentially accelerated moving mirror in coordinate time for the particle production. In addition, the mirror radiation power and radiation reaction force, that have recently been derived, have been applied to the specific moving mirror model of the CGHS correspondence. As a result, it is shown that Larmor power and self-force for the mirror describe quantum radiation. Furthermore, two distinct methods of deriving the stress tensor for the quantum radiation of the moving mirror are analyzed and a comparison analysis is made. Finally, while extensively studying all the known moving mirror solutions and trying to compile collective results, some new results have been found, including some trajectories in null and spacetime coordinates, particle count for the mirrors that have finite particle production, fluxes for some mirrors in certain coordinates that have interesting physical effect, and etc. All existing moving mirror solutions are studied by classification into several types based on their dynamics. Then, each mirror is extensively reviewed from four perspectives: dynamics, flux & energy, particles, and entropy. These methodologies enable one to obtain a complete set of solutions, understand their behavior, and unveil particular implications and physical features of the moving mirror model as a whole.
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    BUILDING INFORMATION MODELING (BIM)-BASED BUILDING SUSTAINABILITY ASSESSMENT FRAMEWORK: CASE OF KAZAKHSTAN
    (Nazarbayev University School of Engineering and Digital Sciences, 2021-03) Akhanova, Gulzhanat
    The building industry has a myriad of adverse effects on the environment that raises the demand for sustainable buildings. Several Building Sustainability Assessment Systems (BSAS) have been developed globally to identify and evaluate the building sustainability level concerning these effects. Building Information Modeling (BIM) is one of the leading directions within the Architecture, Engineering, and Construction (AEC) industry. Despite the various research approaches to facilitate and simplify building sustainability assessment with BIM support, limited research is available on BIM implications of building sustainability assessment methods in developing countries. By considering the regional variations and country-specific differences in terms of assessment factors, this research investigated the integration of BIM and building sustainability in assessing new buildings' sustainability levels. This thesis proposes a conceptual framework that illustrates how BIM can assist in building sustainability assessment. The research concentrated on Kazakhstan's Building Sustainability Assessment Framework (KBSAF) to demonstrate how BIM tools can help assess BSAS compliance projects. This study used the following four-stage methodology to develop the conceptual framework. Firstly, the study reviewed the relevant literature, existing building sustainability assessment methods, and building codes in Kazakhstan to identify the assessment items. Secondly, the research developed a framework for BSAS for commercial buildings in Kazakhstan. In this stage, the study identified nine assessment categories and forty-six indicators applicable in Kazakhstan's built environment. These nine categories are: Construction site selection and infrastructure, Building architectural and planning solutions quality, Indoor environmental quality, Water efficiency, Energy efficiency, Green building materials, Waste, Economy and Management. The identified assessment items were validated using the Delphi technique. In the third stage, weights were allocated to the identified assessment items using the Stepwise Weight Assessment Ratio Analysis (SWARA) technique. The assigned weights were validated through sensitivity analysis for the categories of the assessment framework. Moreover, certification levels were identified and proposed in this stage. In the fourth stage, the research developed a conceptual framework demonstrating how BIM can assist the BSAS. The research methodology to develop the conceptual framework included linking BIM with identified assessment factors and mapping them to previous work. Moreover, it evaluated the BIM potential in assessing the sustainability criteria. The developed framework was validated using a three-round Delphi study with experts from Kazakhstan's AEC industry. The Delphi Experts verified the components of the proposed framework and validated its structure and feasibility. The proposed conceptual framework consists of the following phases: BIM Modeling Phase, Building Sustainability Analysis Phase, and Building Sustainability Assessment Phase. Delphi panelists' overall evaluation feedback indicates that the proposed framework clearly illustrates the phases and BIM functions used to build the sustainability assessment. They also agreed that the implementation sequence is easy to follow and understandable, logical, and practical. Thus, the proposed framework could serve as a systematic guide to BIM application for building sustainability, thus facilitating the assessment process and saving considerable time and effort.
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    Chitosan Composite Cryogel With Polyelectrolyte Complexes for Tissue Regeneration Application
    (Nazarbayev University School of Engineering and Digital Sciences, 2020-09-28) Sultankulov, Bolat
    Chitosan has been a successful choice for tissue-engineering applications over the last few decades. Chitosan is a natural polysaccharide with excellent properties for tissue engineering applications, such as biodegradability, biocompatibility, and antimicrobial activity. Available free amine groups in its structure allows further chemical modifications, so new properties could be added for specific tissue engineering application. This dissertation highlights the advances made in biomaterial production and describes novel polyelectrolyte-based (PEC) cryogel that contains chitosan (CHI) and heparin (Hep). We will discuss the preparation of new cryogel material and its physico-chemical properties. Additionally, the measurement of biological activity would be addressed in vitro and in vivo. In particular, the cryogels obtained will be tested to induce differentiation of mesenchymal stem cells (rat BMSCs) derived from rat bone marrow into the osteogenic lineage. Additionally, this study will show potential uses of novel PEC-based cryogel for skin regeneration in vitro and in vivo, demonstrating the broad application of established scaffolding. The research in this dissertation is important because it demonstrates the efficacy of PEC cryogels for tissue engineering applications. This is the first PEC cryogel scaffold based on CHI-Hep made from a one-step reaction with effective loading of growth factors and cytokines.
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    COMPACT AND ROBUST HIGH EFFICIENCY SHOCK-AND-ERROR-TOLERANT MECHANICAL POWERTRAINS FOR WIND TURBINES APPLICATION
    (Nazarbayev University School of Engineering and Digital Sciences, 2023-10-10) Tariq, Hamza Bin
    Large forces and torques experienced by both small and large wind turbines influence the deflection, loading and overall dynamical response of the gearbox and other powertrain elements, often leading to failure in the drivetrain components. Gearbox-related failures, caused by shocks and misalignments due to gearbox component deflections, generator/grid engagements, etc, are accountable for more than 20% of the wind turbine downtime, resulting in high operational expenditure (OPEX). To address this problem, this thesis presents three main axes of innovation that we propose to be essential for further development of wind turbine technology. The first two parts will introduce novel technologies that are meant to provide an improved wind turbine powertrain system that is robust, compact and efficient. Finally, a material hysteresis model is introduced for system dynamics. This thesis envisions an architecture that will resolve existing limitations constraining the evolution of wind turbine technology and act as an enabler for the vision of possible future wind turbine technologies. First, the substitution of contemporary multi-stage gearboxes in the wind turbine with a more lightweight, compact, robust, and reliable cycloidal drive (speed increaser) of novel design. Traditional transmission gears such as spur or beveloid gears are said to have limitations in power and sizing, and therefore their common alternatives are planetary gears, such as cycloidal gears which have a single-stage transmission ratio. However, current available cycloidal drives, employ a cycloidal teeth profile, which leads to the primary limitations due to dimensional tolerances such as torque ripple and backlash, affecting its efficiency. A possible novel solution is the use of a cycloidal drive fitted with involute teeth mesh. Proper implementation grants superior performance in terms of constant pressure angle and invariance of velocity ratio (more than 1:100) with respect to centre distance alteration. The assessment of these drives is conducted via finite element (FE) and dynamical multi-body analysis, where the stresses, contact forces, torque ripple and efficiency are evaluated. Furthermore, to mitigate the impact of shock on the system, especially in the gearbox, a novel hybrid clutch is introduced that fuses the concepts of dog clutches and disc clutches currently available in the industry. The idea of a hybrid clutches is to couple two rotating shafts by a form-type interface to provide maximum torque/volume ratio, while the disc clutches which are already in use in wind turbine systems have lower torque/volume ratio and are best at absorbing shock transients and used to mitigate the short-term torque peaks by slipping at a defined maximum torque in order to briefly interrupt the drive. This clutch can be placed between the generator and the cycloidal drive. The proposed clutch will have a graded stiffness distribution, achieved by a complex internal topology, so that at low torques and during transients it will present a sliding disc surface interface, whereas at higher loads, by increasing the compressive force between the clutch discs, these will be deformed to a mating ‘wavy’ form-type interface (the equivalent of a dog clutch), characterized by a much higher load carrying capacity, thereby securing high efficiency at high loads. Analysis of this design is done with finite elements to examine its load-carrying capacity and dynamical response. Additionally, mechanical and structural systems' damping behaviour is often described using viscous damping models like the Voigt-Kelvin model. These models can be helpful, but they may not be adequate for analysing complicated structural systems. For this reason, a generalised novel finite element non-linear hysteresis model is developed from the modification of the conventional Kelvin-Voigt model, to produce a non-viscous hysteretic behaviour. The MDOF hysteresis model is based on its instantaneous state under forced vibration and does not need previous information on the history of motion, excitation parameters, or frequency. Currently, the finite element packages do not yet have any damping estimate techniques that are both accurate and reliable. Therefore, such a robust model is required that will yield reliable results regardless of the excitation frequency and will not need special calibration of the model parameters, as the Kelvin-Voigt family of models, including beta-damping, requires. In the design of gears and gearboxes, damping models may be utilised to improve the design, which in turn results in lower levels of vibration as well as noise.
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    CYTOSKELETON DYNAMICS AND SPATIAL ORGANIZATION DURING EPITHELIAL-TO-MESENCHYMAL TRANSITION
    (Nazarbayev University School of Engineering and Digital Sciences, 2024-05-16) Nurmagambetova, Assel
    RATIONALE: Epithelial-to-mesenchymal transition (EMT) is a process that occurs during normal physiological processes (embryogenesis and organ formation) and if it is inappropriately activated it can lead to pathological processes (formation of scars, cancer metastasis, etc.). EMT is well studied at the morphological and transcriptome level. However, cytoskeleton changes during this process are less well understood. The cytoskeleton consists of microtubules, actin filaments, and intermediate filaments. In addition, there are protein complexes named focal adhesions that provide cell attachment to the extracellular matrix, and connect the actin cytoskeleton with the extracellular matrix. To describe the changes in the behavior of the cytoskeleton, namely microtubules and actin cytoskeleton during EMT is of particular interest. In addition, describing the behavior of focal adhesions during EMT is also important. AIM: The objective of this study is to describe quantitatively morphological changes that occurred in post-EMT MCF-7, A-549, and HaCaT cells, analyze microtubule dynamics, spatial organization, and its contribution to cell motility, identify changes in actin filament organization and study focal adhesion turnover. HYPOTHESIS: The dynamics of microtubules in cells undergoing EMT v might change. Cells undergoing EMT are expected to have more dynamic microtubules. Cells undergoing EMT are expected to more efficiently adhere to diverse substrates and therefore better spread. Focal contacts in cells undergoing EMT are expected to be more pronounced and dynamic than in cells not undergoing EMT. METHODS: To study changes in post-EMT cells, EMT was induced in three different cell models: MCF-7, A-549, and HaCaT. To evaluate that EMT happened, western blot and quantitative polymerase chain reaction (q-PCR) were applied to determine the level of expression of master regulators of EMT. Cell images were recorded using bright field microscopy, and analyzed using the Fiji Image J program. In analyzed cells, microtubule networks and actin filaments were visualized by immunofluorescence. To follow, describe, and measure microtubule dynamics transfection with EB-3-RFP protein was conducted. To visualize focal adhesions, two approaches were used: transduction with a talin red fluorescent protein (Talin-RFP) and transient transfection with Ptag-RFP-vinculin. Films were recorded using time-lapse fluorescent microscopy and analyzed using the Fiji Image J program. All statistical analysis was performed using GraphPad Prism (Dotmatics, USA) and a nonparametric Mann-Whitney U test or parametric t-test with Welch correction. The actin filament measurements were vi completed using Matlab scripts. CONCLUSION: This study showed morphological changes in three post- EMT cell cultures studied. All types of cells increased in size. MCF-7 and HaCaT became spread out, while A-549 became elongated. All three post-EMT cell cultures had changes in microtubule organization and dynamics. Post-EMT MCF-7 and HaCaT cells showed microtubules at a low density at cell borders, while post-EMT A-549 cells had less covered nuclei by microtubules. In all three studied models, the microtubule growth rate increased and the length of the microtubule plus end tracks became longer. The average angle of microtubule growth trajectories to cell radius decreased. Actin fibers rearranged into stress fibers in post-EMT cells. The area of focal adhesions decreased in all post-EMT cell cultures studied and focal adhesions appeared localized throughout the inner areas of spread cells. These results indicate that cytoskeletal changes make a significant contribution to the EMT process.
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    DELIVERY OF MONOCLONAL ANTIBODIES FROM MICROENCAPSULATED CELLS
    (School of Engineering and Digital Sciences, 2023) Ashimova, Assem
    The use of monoclonal antibodies (mabs) is a promising therapeutic approach for the prophylaxis and treatment of a wide range of illnesses, including cancer, autoimmune, and infectious disorders. They currently rank among the most widely used drugs in the pharmaceutical sector. The area of medicine where mabs are most extensively employed is oncology. Unfortunately, the complicated and costly nature of mab design, mab secretion, and purification are prohibitive and pose a hurdle to product development and pre-clinical modification, which is a significant obstacle to the use of mab therapy in clinical practice. Additionally, parenteral mab administration also poses clinical difficulties. Patients experience mild-to-moderate injection site and infusion-related responses, despite mab therapy having a low overall reactogenicity. Here, we proposed that mabs might be efficiently given by allogeneic cells that produce mabs and are encapsulated to increase cell viability and safeguard against host immunological reactions. Various illnesses, such as diabetes mellitus, anemia, cancer, and neurodegenerative disease, have been successfully treated in animal models and people through the delivery of therapeutic drugs by microencapsulated single-cell populations. A single injection of microcapsules is anticipated to be effective since the microcapsules can be tailored to last for the duration necessary for the treatment by altering the concentration of alginate and the cross-linking of alginate with PLL. While preventing immune cells from attacking the enclosed cells, the biocompatible membrane permits a bidirectional flow of nutrients, oxygen, and waste products. When a slow, continuous mab release over a lengthy period of time is necessary, cell encapsulation-aided mab delivery is preferable to bolus mab injection. Therefore, in this pre-clinical model, we investigated the feasibility of mab administration utilizing an enclosed cell culture that expresses mab. Until now, transformed hybridoma cells have been used to produce and secrete mabs. The novelty of this study is the use of non-professional immune cells, such as murine G8 myoblasts and human HEK293 (human embryonic kidney cells) cells, to secrete mabs. These cells were transfected with plasmids that encode the heavy and light chains of human IgG specific for antigens relevant in treating cancer and COVID-19 and then enclosed in alginate microcapsules. Afterward, immunocompetent (C57/BL6J) mice were intraperitoneally implanted with the microcapsules, and changes in the level of circulating mab were evaluated. Western blotting, ELISA, and microscopy were used to characterize the mab both in vitro and ex vivo. Co-transfected G8 cells secreted intact IgG at sustained levels similar to transfected HEK293 cells. Partial characterization of the secreted mab was performed. Mice implanted with 4 microcapsules containing G8 cells secreting mab induced the detection of blood mab for 40 days. This study shows the feasibility of cell microencapsulation for the systemic delivery of intact mab. This method has potential significant therapeutic applications that call for further investigation.
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    DESIGN AND ENGINEERING OF ADVANCED SI-BASED THIN FILM ANODE MATERIALS FOR LI-ION BATTERIES
    (Nazarbayev University, School of Engineering and Digital Science, 2019-06-17) Mukanova, Aliya
    Lithium-ion batteries (LIBs) are a versatile way of energy conversion and storage. Thin film batteries are the next generation of Li-ion battery technology with the thickness of tens μm and aimed to power a diverse range of microdevices. In order to increase the storage possibility, i.e. capacity, of such batteries, new high capacity electrode materials should be developed. Silicon-based materials are the most promising anodes due to the highest theoretical capacity and a low potential. However, the current drawbacks of Si such as significant volume expansion, electrical contact loss, and low conductivity impede its practical application in LIBs and commercialization. In this doctoral thesis, the research has been performed in two main directions in order to improve the existing microbatteries and find a way to develop a stable Si-based thin film electrode. The first direction is an investigation of novel silicon carbide thin film (3C-SiC) with a cubic lattice as an anode for LIBs. The advanced method of "single" particle measurement for studying the electrochemical properties of an individual microparticle provided the new data which allowed suggesting the mechanisms of lithiation/delithiation in 3C-SiC film. The use of XRD, TEM, XPS, Raman spectroscopy confirmed that there was no degradation of the 3C-SiC crystal lattice. The obtained results demonstrated that there are in two possible reasons of 3C-SiC thin film electrochemical activity, an intercalation or a capacitance. The second direction is the design of the three-dimensional (3D) amorphous Si (a-Si) thin film anode. The improvement of a-Si thin film anode was achieved through studying the effects of substrate surface condition, dopants incorporation, electrolyte additive and addition of graphene (GF) underlayer. The designed n-type doped porous a-Si thin film and 3D a-Si/GF anode exhibited high electrochemical performance in the lithium cells for several hundred cycles.
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    Design of Aptamer-Functionalized Substrates: Towards Breast Cancer Stem Cell Isolation and Detection
    (Nazarbayev University School of Engineering and Digital Sciences, 2020-09-25) Bekmurzayeva, Aliya
    Cancer relapse and metastasis remain one of the main problems in treatment of breast cancer (BC). A small subset from bulk tumor cells, called breast cancer stem cells (BCSCs), is found to be responsible for cancer initiation, recurrence, metastasis and resistance to therapy. Therefore, specifically detecting these cells is an important task in BC diagnosis and management. The main goal of this thesis was to develop aptamer-functionalized substrates which in the future could be used for BCSC isolation and detection. To achieve this objective, the project has been divided into three tasks as will be discussed below. Given small number of available specific ligands against BCSC and their importance in BC, one of the tasks of this thesis was to select and characterize new single stranded DNA aptamers against BCSC. Fluorescently activated cell sorting was utilized to enrich oligonucleotides bound to cells while imaging flow cytometry was used to study their binding. Two of the selected aptamers showed increased binding to target cells than to control cells; however, their binding affinity was not fully studied. They are one of the few ligands reported to date to bind BCSC and were selected against well characterized BCSC derived from a triple-negative breast cancer. Another task of this work was to functionalize stainless steel (SS) wire with aptamers specific to BCSC in order to alleviate the problem of “fishing out” such rare events as BCSC. For this, the wire electropolishing conditions were determined. In order to attach ligand, silanization by electrodeposition was optimized thus determining the most suitable applied potential (–0.8 V), pH of the solution (pH 5 and 5.5) and heat treatment temperature after electrodeposition (130°C). The silanized surface was then immobilized with commercially available CD44 aptamers (marker of BCSC) after being activated by a crosslinker to build a functionalized surface. This wire was able to capture the target cells in an in vitro test. The wires were analyzed by such surface characterization methods as atomic force microscopy (AFM), cyclic voltammetry (CV), scanning electron microscopy (SEM) and fluorescence microscopy. In addition, using the same surface chemistry as in functionalized SS wire, another platform – fiber Bragg grating (FBG) sensor has been explored with a well-studied ligand-analyte pair (thrombin and thrombin-binding aptamer). For this, FBG was made sensitive to the surrounding refractive index (RI) by chemical etching and calibrated in solutions with known RI before being functionalized with aptamers. Then the sensor demonstrated increased Bragg wavelength shift when tested in different thrombin concentrations. In conclusion, the main goal of this thesis – developing aptamer-functionalized substrates with a perspective application in BCSC isolation and detection – was achieved, although each task of the project was completed with different level of success. Binding of aptamers selected against BCSC could not be fully studied. However, they are one of the few reported aptamers against an important subtype of BC. Besides, only a small fraction of aptamer candidates were characterized and better binders could still be revealed. Wires functionalized with CD44 aptamers, after further study, have a potential to be used for in vivo capture of target cells in the blood flow, since their small size allows the insertion as a standard guidewire in biomedical devices. For fabricated EGBF biosensor, selective detection of clinically relevant concentration of thrombin has been demonstrated. The used functionalization method allows a facile fabrication of the sensor not requiring thin film fabrication.
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    DESIGN OF CMOS-MEMRISTOR CIRCUIT OF LSTM ARCHITECTUR
    (Nazarbayev University School of Engineering and Digital Sciences, 2021-03) Smagulova, Kamilya
    The growing amount of data, the dawn of Moore's law, and the need for machines with human intelligence dictated several new concepts in computing and chip design. Existing physical limitations of Complementary Metal-Oxide Semiconductor (CMOS) transistors and von-Neumann bottleneck problems showed that there is a need for the development of in-memory computing devices using technologies beyond-CMOS. The architecture of the long short-term memory (LSTM) neural network makes it an ideal candidate for modern computing systems. Recurrent connections and built-in memory of the LSTM network also allow us to process different types of data, including ones with temporal features and dependency. The realization of LSTM, and other artificial neural networks (ANNs), implies a large amount of parallel computations. Therefore, in most cases, their training and inferencing are implemented on modern computing systems with the help of a graphical processing unit (GPU). In addition, there are several available solutions for energy and area efficient inference of neural networks based on field-programmable gate arrays (FPGA) and application-specific integrated circuits (ASIC) platforms in both digital and analog domains. In 2008, the discovery of a new device called 'memristor’, which acts as an artificial synapse, brought attention to developments of memristive artificial neural networks ANNs. Due to their nanoscale size and non-volatile nature, memristor crossbar arrays (MCA) allow several orders of magnitude faster dot-product multiplication and require a smaller area and lower energy consumption. The recent successful works where memristors were used as a dot-product engine include “A convolutional neural network accelerator with in-situ analog arithmetic in crossbars” (ISAAC) and “A programmable ultra-efficient memristor-based accelerator for machine learning inference” (PUMA). Nevertheless, training of ANN on FPGA and ASIC remains a challenging problem. Therefore, the majority of memristive platforms are proposed only for the acceleration of neural networks with pre-trained parameters. In this thesis work, the design of an analog CMOS-memristor accelerator implementing long short-term memory (LSTM) recurrent neural network at the edge is proposed. The circuit design of a single LSTM unit consists of two main parts: 1) a dot-product engine based on memristor crossbar array using “one weight -two memristors” scheme; and 2) CMOS circuit blocks used to realize arithmetic and non-linear functions within LSTM unit. The proposed design was validated on machine learning problems such as prediction and classification. The performance of the analog LSTM circuit design was compared with other types of neural networks and neuromorphic systems, including a single perceptron, FNN, DNN, and modified HTM. Besides, analyses of memristor state variability in hybrid CNN-LSTM and CNN implementation for image classification have been performed successfully.
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    DESIGN, MOTION PLANNING, AND CONTROL OF A SPHERICAL PARALLEL MANIPULATOR WITH COAXIAL INPUT SHAFTS
    (School of Engineering and Digital Sciences, 2023-06) Tursynbek, Iliyas
    A special class of parallel manipulators, known as spherical parallel manipulators (SPMs), can offer a t hree d egrees-of-freedom ( 3-DOF) p ure rotational motion. Among the various 3-DOF SPMs that have been developed, the most commonly used one is the 3-RRR type SPM. Many applications, such as machining, target tracking, and object stabilization require full-circle or infinite roll rotation from an SPM. Hence, this thesis aims to design a coaxial SPM with an infinite r oll r otation f eature, which could b e used in such applications. The thesis starts with a kinematic analysis of the proposed coaxial SPM model, providing methods and algorithms for obtaining unique solutions to the forward and inverse kinematic problems. Then, it is followed by a numerical estimation of its Cartesian workspace and configuration space ensuring no singular configurations o r c onfigurations wi th li nk co llisions. To pe rform li nk collision checks, a simulation model of the coaxial SPM was created in the CoppeliSim robot simulator for the first t ime. It was found that for the given SPM geometry its maximum tilt is equal to 39∘. Then, a convex approximation of the obtained configuration s pace w as obtained. Subsequently, it was used in a constrained control for external target tracking with joystick, and object stabilization based on the IMU orientation sensor measurements. Results presented in this work are supplemented with examples, and the final prototype of the functional motion control system based on a coaxial SPM structure is presented.
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    DETERMINANTS OF INNOVATION: AN EVIDENCE-BASED PERSPECTIVE IN THE DIGITAL TRANSFORMATION ERA
    (School of Engineering and Digital Sciences, 2023) Akhmadi, Saltanat
    Innovation, the process of finding and using new ideas, creating new products or services, and introducing them to the market, is widely celebrated as the driving force of economic growth, sustainable development, and social change. Yet, innovation activity around the world is mostly concentrated in a few leading countries that possess the human and financial capital to create new knowledge and the market acumen to capitalize on it. For instance, three quarters of the patent filings from global innovation hotspots are emerging from just four countries – USA, Japan, China, and Germany. This uneven concentration of inventive activity, aptly named the global innovation divide, increases the gap between developed and developing economies. The situation has exacerbated over the last decade with the fourth industrial revolution and the emergence of the digital economy that has brought to the forefront knowledge generation and utilization. To close the innovation gap, regional, national, and international governments and authorities constantly encourage innovation through an array of fiscal subsidies and regulatory interventions with admittedly mixed results. Innovation of course starts at the firm level, with innovative firms developing competitive advantages for themselves and for their regions through knowledge exploration and exploitation and the creation of new technologies. Even in innovation leaders such as Germany, roughly one in two enterprises do not engage in innovation. Obstacles to innovation reflect the realization that innovation is a difficult, financially risky, and mostly liable to fail process. A multitude of business surveys and research studies have been dedicated to identifying and assessing the importance of the obstacles that deter firms from innovating and contrasting them with the obstacles slowing down, but not stopping, firms already engaged in innovation. While there is a broad consensus on what constitutes an obstacle to innovation, the term is open to a wide range of interpretations that are largely contingent upon the context within which innovation occurs. This handicaps the effectiveness of innovation policies that are based upon a generic understanding of the innovation process and are not sufficiently nuanced for the digital era. Past research on innovation has sought to identify major correlates of innovation by assessing only one dimension of innovative behavior at each time. Treating the phenomenon of innovation as unidimensional does not sufficiently capture the richness of the construct of organizational innovation. This dissertation demonstrates instead that the process of innovation is decidedly multi-dimensional and explores the multi-faceted nature of the impact of innovation on firms, regions, and countries. Based on an extensive range of iv publicly available datasets and using a multi-dimensional analytical approach, this dissertation dissects the phenomenon of innovation at several layers of abstraction: the firm layer, the operational layer, the process layer and the policy layer. The contributions of this dissertation at each layer are addressed in turn. • At the firm layer, the key characteristics of the profile of an organization that impact its involvement in innovation activities are identified as firm size, sector, and prior engagement in innovation activities. • At the operational layer, the effect of factors present in the operational environment within which innovation occurs is measured with emphasis on economic, market, cultural and gender diversity issues. • At the process layer, issues related to knowledge acquisition, elicitation, and management in innovative firms are introduced and examined in the context of tangible innovation outputs such as intellectual property rights. • At the policy layer, the effect of innovation policies and interventions over the last decade is assessed with a special focus on the promotion of clustering activities and innovation hotspots. The results of this evidence-based dissertation presented herein are instrumental in defining the specific facets of an effective, modern innovation policy, producing the desired performance outcomes in a context of limited resources for innovation.
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    DEVELOPMENT AND OPTIMIZATION OF ML BASED COMPREHENSIVE MODELLING FRAMEWORK FOR GAN HEMTS
    (Nazarbayev University School of Engineering and Digital Sciences, 2024-04-24) Saddam Husain
    Radio Frequency (RF) Power Amplifier (PA) is one of the most pivotal constituents of any wireless transceivers. However, continual advancements and ever-increasing complexity in the wireless communication technologies demand frequent innovations in the design of RFPAs. The quality of the designed RFPAs are generally evaluated based around two basic figures of merits namely efficiency and linearity. Thus, the RFPAs should provide maximum power and efficiency while maintaining highly linear operation. In literature, two primary PA design mechanisms, namely measurement- and modeling-based techniques have been extensively utilized. Each class of technique has pronounced merits, limitations and applications. However, owing to the seamless integration ability of the modeling-based techniques with Computer-Aided Design (CAD) tools, they are increasingly becoming more popular. The design and innovation in RFPAs are excessively contingent on the measurement facilities and the Large Signal Models (LSMs) of transistor devices. At present, Gallium Nitride (GaN) High Electron Mobility Transistor (HEMT) technology is regarded as an optimal microwave transistor technology for the design of RFPAs in advanced RF/microwave and high power switching applications. This is due to their attributes namely high energy bandgap, high saturation velocity, high electron mobility, exceptional thermal behavior and high breakdown field. Furthermore, GaN HEMTs manifest high power density, thus a smaller size device can be used to sustain a high power demand. It also implies reduced lower capacitances and lower combining losses in the design of RFPAs and Low-Noise Amplifiers (LNAs). At this point, it is essential to mention that, in general, the available LSMs of GaN HEMTs are very specific and therefore not readily useful for broad range of PA designs. Therefore, there is a pressing requirement to develop accurate, reliable, efficient and robust LSMs of GaN HEMTs which can be readily incorporated in CAD tools. Nevertheless, Small-Signal Model (SSM) development is the first step in pursuit of developing accurate and efficient LSMs. But, both SSMs and LSMs of GaN HEMTs are essential for the design of accurate, efficient and reliable GaN HEMT based RFPAs. Apparently, various modeling schemes have been exploited to develop SSMs and LSMs for GaN HEMTs, however, usually, they are classified into three main groups, which are physics-based, Equivalent Circuit (EC) and Behavioral Modeling (BM) frameworks. This thesis is originated in response to the scientific and technical challenges in EC and BM frameworks for GaN HEMTs at high frequency applications. Among these challenges, the major focuses are on the development of SSMs for GaN HEMTs, which are simple, accurate, computational and time efficient, reliable, scalable, and CAD adaptable. Furthermore, special attention is given to develop SSMs, which manifest strong interpolation and extrapolation abilities. The developed SSMs are then utilized to realize the eventual LSMs for GaN HEMTs. In order to develop SSMs and LSMs for GaN HEMTs, which possess the above-mentioned characteristics, in this thesis, Machine Learning (ML) based approaches have been explored and utilized because of their superior learning, prediction, and extrapolation abilities. However, it is pertinent to state that the ML based modelling of GaN HEMTs is still in its early exploration phase, and various issues related to this type of modelling are unexplored and not thoroughly discussed in literature. It is therefore, in this thesis, an extensive appraisal and analysis of ML and optimization based small-signal and large-signal modelling for GaN HEMTs have been presented. In the first part of this thesis, a detailed comparative analysis of EC based accurate, robust and efficient SSM parameter extraction methodologies for GaN-on-Diamond HEMTs has been demonstrated. For this, initially, a Scanning- Based Systematic (SBS) model parameter extraction approach is developed and applied on GaN-on-Diamond HEMTs. Thereafter, marine predators algorithm, pelican optimization algorithm and tunicate swarm algorithm, the recently developed Optimization Algorithms (OAs), based hybrid extraction methodologies have been developed and applied on the same GaN HEMTs. Finally, a detailed comparison of OAs and SBS modelling schemes by using SBS extraction approach as a benchmark in terms of reliability, accuracy, convergence behavior, complexity, execution time, and scalability is provided and thoroughly discussed. Accurate, efficient and CAD compatible small-signal behavioral models for GaN HEMTs using Artificial Neural Network (ANN), Support Vector Regression (SVR) and Gaussian Process Regression (GPR) based ML techniques have been developed, validated and discussed in the subsequent part of this thesis. These ML based approaches have been applied on many GaN HEMTs devices grown on Silicon (Si), Silicon Carbide (SiC) and Diamond substrates. Furthermore, a meticulous evaluation of ANN algorithms implemented in MATLAB, Python (using Keras, PyTorch and Scikit-learn) and R (using H2O) for small-signal behavioral modelling of GaN HEMTs has been presented. To establish the appropriateness of software environments in distinct application settings, the developed models are examined on a range of metrics namely behavior on the unseen data, training and prediction speed and ADS adaptability, and software environments are surveyed for support and documentation, user-friendly interface, simplicity in the model development procedure, open-access and cost. Optimization of the hyperparameters of ML algorithms is vital to realize the best possible models. In this context, hybrid optimized ML algorithms namely Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) assisted ANN, PSO and RSO assisted SVR, RSO assisted GPR, and RSO assisted various tree-based models are explored and developed. Finally, the developed models are evaluated on many regression tests to identify the most fitting ML algorithms for particular applications. Finally, the last part of the thesis presents ML based CAD adaptable advanced models and applications. Initially, GPR, GA-ANN and RSO-Decision trees based SSMs for GaN HEMTs are developed. Then, the integration of these developed models with ADS are presented by inserting the developed ML based models into a design of class-F PA. Subsequently, to examine the accuracy of the models, stability and gain tests of the GaN HEMT based amplifier configuration are performed. Thereafter, using the developed SSMs, a joint EC-behavioral LSM for a GaN HEMT is developed and presented. The intrinsic drain and gate currents are modelled using GA-ANN, PSOSVR and GPR based approaches. These current modelling approaches are compared in terms of simplicity in the model development stage, computational efficiency, accuracy and required time to simulate the currents. At last, LSM validation and realization using GA-ANN based approach are demonstrated on a design of an inverse class-F PA.
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    DEVELOPMENT OF A PATIENT-SPECIFIC OCULAR MODEL FOR RISK ASSESSMENT OF GLAUCOMA DEVELOPMENT AND PROGRESSION
    (Nazarbayev University School of Engineering and Digital Sciences, 2020) Kharmyssov, Chingis
    Glaucoma is the leading cause of blindness worldwide. Once the retinal ganglion cell axons are lost they cannot be cured. Therefore, preventative risk assessment measures are important. To be able to perform these tasks, one needs to understand the mechanism behind the axonal blockage that leads to glaucoma. Biomechanical factors are thought to play a role in glaucoma, but the specific mechanism is not explored. In a Finite Element (FE) ocular model, the complex shape of the optic nerve components can be modeled and relevant mechanical quantities, such as stresses and strains due to intraocular (IOP) and/or intracranial (ICP) pressure, can be estimated and their effects assessed. Furthermore, optic nerve head (ONH) morphology and especially lamina cribrosa shape and properties, which are tightly linked to Glaucoma onset and development, vary greatly between individuals. This consequently suggests the development of patient-specific FE ocular models. A method to generate patient-specific ocular models was contrived based on the geometry extracted from Optical Coherence Tomography (OCT) scans. Specifically, retinal layers were segmented using intensity and graph-based algorithms and the segmented layers were then reconstructed with a thin plate spline method. Finally, solid models were created from the reconstructed surfaces and meshed with tetrahedral elements. The geometric details of the generated ONH model correlate well with those of generic models from pertinent literature and special attention was paid to meshing so that the optic nerve region of the ocular model exhibits analysis-suitable element quality. The suggested reconstruction method is semiautomatic and although we aimed to fully capture the complete ONH region, some anatomical structures, which are generally considered relevant and important, could not be extracted from OCT images in vivo. These include the pia arachnoid complex (dura mater and pia mater) that contains cerebrospinal fluid material and is considered to exert ICP. These were handled by carrying out a parametric analysis, using generic models with linear elastic material properties, to establish the degree of importance of the pia arachnoid complex. It was found that pia and dura mater properties can affect post laminar neural tissue and lamina cribrosa biomechanics. As it is currently infeasible to obtain high-quality patient-specific geometries for the pia arachnoid complex in vivo, we embed generic models of the pia and dura mater in our patient-specific ONH model. Viscoelastic material properties of dura mater and sclera were additionally retrieved from physical unidimensional tensile stress-relaxation tests. The influence of viscoelastic material properties at certain levels of ICP/IOP with a generic ocular model was examined, and results indicated, as expected, the importance of viscoelastic properties. Parametric analysis of patient-specific models was performed via the principal component analysis method deriving statistical shape models (SSM). Qualitative, quantitative and biomechanical assessments were performed with the aid of the generated SSM. For the biomechanical assessment, finite element modeling was employed and several patient-specific models, based on SSM shape modes, were generated and tested. We anticipate further enhancements and developments for this approach in the future. Based on the so far obtained results, we find evidence that patient-specific, anatomically detailed 3D ocular models allow for a better understanding of employed biomechanics and can benefit glaucoma risk assessment.
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    DEVELOPMENT OF ADVANCED ELECTROLYTES FOR ALL-SOLID-STATE Li-ION/Li METAL BATTERIES
    (Nazarbayev University School of Engineering and Digital Sciences, 2022-04-28) Tolganbek, Nurbol
    Accelerated development in areas of portable electronics, electric transport and renewable energy requires safe and reliable rechargeable batteries. Currently available secondary batteries cannot provide necessary performance and suffer from rapid degradation and loss of stability. One of the most promising systems 'close' to this niche are the all-solid-state batteries (ASSBs). Besides thermal, mechanical, chemical and electrochemical stability, introducing solid electrolytes into battery provides other advantages such as prolonged cycle life and, most importantly, the use of Li metal as anode to achieve the highest energy density. In this research, NASICON-structured Li1.3Al0.3Ti1.7(PO4)3 (LATP) ceramic and ultra-thin (PEI/PAA)30 gel polymer electrolyte (GPE) have been studied as solid lithium ion conducting materials. The correlation between morphology and ionic conductivity in LATP ceramics was revealed by fabricating through various synthesis routes and optimization of their procedures. Formation of reported impurity phases has been resolved through alternating precursors and adding extra lithium salts to prevent its loss during the calcination. The most optimal calcination temperature was identified for high pure LATP ceramic for various fabrication methods. Despite different synthesis routes, the crystal cell volume of all materials was similar; however the shape and size of the particles significantly differed. Well-defined cubic shaped LATP with larger grains tend to have higher ionic conductivity due to a great densification degree. The main problem of LATP, reduction by Li metal, was resolved by applying protective layers. Interlayers made of lithium ion conducting polymer materials are the most promising due to mechanical stability and ability to prevent dendrite growth and simplicity of preparation. Coating LATP pellets with ultra-thin artificial layer with composition of (PEO/PAA)30 was found to be very effective. The interlayer deposition by layer-by-layer (lbl) technique prevented the side reaction and decreased interfacial impedance at the electrode and electrolyte boundary. Symmetric cell with Li metal and polymer coated LATP electrolyte performed over 1 500 hours at 0.5 mA/cm-2 demonstrating an outstanding performance. The lbl technique was also utilized to form thin gel polymer electrolyte on electrodeposited Ni-Sn alloy anode on a Ni foam to design a 3D full cell. Mechanically strong ultra-thin electrolyte evenly coated the surface of 3D electrode providing high lithium ion conduction, and the full cell demonstrated stable performance of 100 galvanostatic charge-discharge cycles with a capacity retention of 90% at 0.1 mA cm-2.