7264 research outputs found
Sort by
MIND MATTERS: EXPLORATION OF MENTAL HEALTH PERCEPTIONS AND HELP-SEEKING BEHAVIORS AMONG UNDERGRADUATE STUDENTS IN KAZAKHSTAN
No full textThis study explores perceptions and attitudes, as well as help-seeking behaviors in the context of mental health within the theory of intersectionality among undergraduate Nazarbayev University students. The research employed a qualitative design, conducting in-depth interviews and using thematic analysis to analyze the collected data. The results demonstrated that students have a comprehensive understanding and awareness of mental health, positively influenced by the university initiatives, social media, and religion. However, such intersecting identities like gender, socioeconomic status, religion, and experiences of stigmatization largely hinder help-seeking. B revealing marginalized groups in the context of seeking mental health support, the research study recommends considering these intersecting elements of identities in addressing mental health to ensure a more inclusive and effective approach.
The results illustrate that students of Nazarbayev University recognize the important role of mental health, including a positive outlook on it. The data demonstrates that although there is a uniformly positive notion about this concept, the way people conceptualize and explain it differs, influenced by professional aspirations, social media, and religion. Students also expressed an alarming tendency they observe in the broader society of Kazakhstan, exemplified by their personal experiences of facing negative societal perceptions about mental health, explained as lack of awareness and stigmatization rooted in labeling and stigma from the Soviet period. Despite facing negative attitudes in the past, interviewed participants are proactive in seeking help themselves, either through professional means, seeking guidance in informal sources, or finding peace in self-help sources like religion
CHARACTERIZATION AND PERFORMANCE ASSESSMENT OF SIO2-KCL-XANTHAN NANOCOMPOSITE AS A NOVEL NANO-ASPHALTENE PRECIPITATION INHIBITOR UNDER LABORATORY CONDITIONS
No full textIn this experimental research work, efficiency of SiO2-KCl-Xanthan nanocomposite (NC) as a nano-inhibitor for adsorption and removal of asphaltene from a synthetic crude oil medium was investigated. The NC has been used as an EOR and smart drilling fluid agent with impressive results. This was the motivation behind this research work. The first phase of the research involved extraction of asphaltene from a West Kazakhstani heavy crude oil and characterization of both asphaltene and the NC. Different state of the art analytical techniques including scanning electron microscopy (SEM), Fourier-transform infrared spectroscopy (FTIR), Brunauer-Emmett-Teller or BET, X-ray diffraction (XRD), and thermogravimetric or TGA were used for NC. This was to ensure authenticity and functionality of the NC. The NC has a spherical structure with particle sizes ranging from 30 to 300 nm determined using SEM analysis. The crystallite size was calculated 41 nm using the XRD data. The surface area of the NC was determined 31.95 m2/g using the BET method. TGA analysis showed that the NC did not experience any significant mass loss for a typical reservoir temperature (80°C) and it is thermally stable for oilfield applications. Based on the FTIR spectra, presence of organic functional groups of phytochemicals on the NC was identified indicting successful synthesis of the NC. The last stage was to assess the efficiency of the nano-inhibitor by determining the Asphaltene Onset Point (AOP) using UV-vis spectroscopy technique and asphaltene adsorption kinetics isotherm modeling using the supernatant obtained from TGA analysis. TGA analysis confirmed that oxidation of virgin asphaltene started at around 400 to 450℃. While, oxidation of 5,000 ppm sample with NC started at around 280℃. The NC has catalyzed oxidation of the asphaltene. Adsorption kinetics isotherm modeling was done using the Langmuir (R2 = 0.98) and Freundlich (R2 = 0.82) isotherm models. The experimental data matched well both models, which suggests monolayer and multilayer adsorption behavior for adsorption of asphaltene onto the surface of the NC. A maximum adsorption capacity of 1.33 mg/m2 was obtained for the NC. The novel nano-inhibitor shifted the AOP by 5% and the optimum concentration of NC was determined 0.3 wt%. Overall, the NC showed promising inhibitory performance under laboratory conditions
DATA AUGMENTATION AND TRANSFER LEARNING IN DETECTION OF COVID-19
No full textGlobal healthcare systems are facing unprecedented challenges as a result of the
COVID-19 pandemic, which calls for quick and precise diagnostic instruments to stop its
spread. In this study, we investigate the application of transfer learning in convolutional
neural networks (CNNs) and the influence of data augmentation in de- tecting COVID-19
from chest X-ray images. We leverage a dataset comprising images from patients
diagnosed with COVID-19 and images corresponding to non-COVID cases, and as part
of a transfer learning approach, we utilize four well known CNN models: ResNet50,
MobileNet V2, EfficientNet V2, and MobileNet V3. A key focus of our research is the
systematic investigation of data augmentation factors and their impact on model
performance. Through varying the intensity and types of data aug- mentations, such as
rotations, flipping and zooming, we seek to optimize the models’ ability to generalize
from training data to real-world scenarios.
Our findings reveal that precise calibration of data augmentation significantly en hances the diagnostic capabilities of the models. While increased augmentation gen erally improves sensitivity and specificity, excessive augmentation diminishes mod- els’
performances due to overfitting on non-realistic features. As another result, MobileNet
V2 and MobileNet V3 show the highest specificity scores of 0.72 and 0.70, while
EfficientNet V2 demonstrates superior sensitivity of 0.96, indicating the strengths and
trade-offs of different architectures. These results, assessed through a comprehensive set
of metrics including accuracy, sensitivity, specificity, precision, recall, F1 score, and
AUC-ROC, underscore the effectiveness of deep learning meth- ods in COVID-19
identification and the crucial role of tailored data augmentation in improving model
robustness. The implications of our results extend to clinical prac- tice and public health,
highlighting the potential of integrating advanced machine learning technologies into
healthcare workflows to enhance diagnostic efficiency and patient care. Looking ahead,
we propose further exploration into additional imaging modalities, the integration of
multi-modal data, and more sophisticated data aug- mentation techniques, such as usage
of Generative Adversarial Networks (GANs), to refine diagnostic accuracy. Overall, our
study reinforces the significance of transfer
learning and deep learning in addressing the urgent challenges posed by infectious diseases
like COVID-19, paving the way for more sophisticated diagnostic tools
EXPLORING ENGLISH FOREIGN LANGUAGE ANXIETY AND ACADEMIC MOTIVATION: A QUALITATIVE CASE STUDY OF UNDERGRADUATE STUDENTS’ PERSPECTIVES AND EXPERIENCES IN KAZAKHSTAN
No full textThere is an increasing amount of research conducted on foreign language anxiety (FLA),
yet the studies conducted in the Kazakhstani context are still very few (Duysembekova &
Kurban, 2022; Myrzakulova, 2019; Plyushko, 2018). The purpose of this study was to explore the factors influencing FLA and academic motivation. By exploring the factors contributing to FLA, the qualitative study seeks to uncover the root causes and triggers of this anxiety in one English medium instruction (EMI) university in Kazakhstan. The study also aimed to understand the nuances influencing English as a foreign language (EFL) students’ learning journey and academic motivation in EMI context. The findings reported that social and psychological factors impacted students’ FLA and academic motivation. This including socio-environmental factors such as pedagogical, teacher- student relationship, and peer impact. Understanding the underlying causes of FLA concerning academic motivation in EFL undergraduate students is crucial for educators, educational institutions, and policymakers to develop targeted strategies and support systems that can enhance a positive learning experience.
Keywords: foreign language anxiety, EFL (English as foreign language), academic
motivation
EXISTENCE AND UNIQUENESS RESULTS FOR A SYSTEM OF COUPLED KDV EQUATIONS IN WEIGHTED SOBOLEV SPACE
No full textThe system of Korteweg–De Vries (KdV) equations is a system of
partial differential equations. It is used to describe the modeling of shallow water
surface waves. In this paper, we show the well-posedness of the coupled Kor-
teweg–De Vries system of equations with initial data under Sobolev Space and
Weighted Sobolev Space for 3/4 ≤ s ≤ 1
KRAS MUTANTS, A KEY ONCOGENE IN NON-SMALL CELL LUNG CANCER
No full textCancer, a disease with a high risk of mortality, continues to pose a significant global health challenge, affecting millions of individuals every year. Non-small cell lung cancer (NSCLC), which accounts for 80–85% of lung cancer cases, is one of the most prevalent cancer types (Xie et al., 2021). The Kirsten rat sarcoma viral oncogene homologue (KRAS) is one of the most commonly mutated genes in NSCLC. KRAS encodes a protein that has both active and inactive states. In cancer, this protein remains persistently active, leading to angiogenesis and tumorigenesis (Matsuo et al., 2009). More than 80% of KRAS mutations are found at codon 12, with the most prevalent mutations being KRAS G12C (40%), KRAS G12V (18–21%), and KRAS G12D (17–18%), among others (Xie et al., 2021). Current research on G12C inhibitors has shown promising cancer therapy candidates such as sotorasib and adagrasib (Kwan et al., 2022). However, acquired resistance to inhibitors of the G12C mutation has been observed over time (Blaquier et al., 2021). Therefore, it is necessary to generate KRAS mutants that can be used for drug screening to analyze the efficacy of drugs and resistance mechanisms.
The purpose of this project is to use CRISPR/Cas9-mediated introduction of mutations to develop KRAS mutant models with G12C, G12V and G12D mutation in the H1299 cell line. KRAS mutants further can be used to screen new drug candidates for the KRAS-dependent NSCLC. The motivation behind this effort is the generating cell lines with endogenous mutations by homology-directed repair (HDR) in the KRAS second exon that will mimic natural conditions and show more physiologically relevant data, which is necessary to analyze the effectiveness of different treatments. In summary, this project tested the efficacy of generating knockout of the KRAS gene and the introduction of G12C, G12V and G12D mutations to the second exon of KRAS in the H1299 cell line using CRISPR/Cas9 system. The results of the research project showed the low efficacy of HDR to generate endogenous mutations and the successful application of CRISPR/Cas9 to generate KRAS knockout clones
CALCULATIONS OF VALUE AT RISK FOR THE PORTFOLIO OF 5 S&P 500 STOCKS USING 5-DIMENSIONAL COPULA FUNCTIONS
No full textThe purpose of this project is to compare copula estimations of Value at Risk (VaR) for a portfolio of 5 S&P 500 stocks to historical, normal distribution, and Monte Carlo methods employing dependence measures and ARIMA-GARCH time series models. This study will provide interpretations of financial data between 2019-2024 in a scope of 5 equations: Gaussian, Clayton, t-Copula, Gumbel, and Frank copulas. Correlations between closing prices of the largest 30 S&P 500 companies by market capitalization were calculated, and the portfolio was constructed by selecting 5 stocks with the least average correlation. The Markowitz portfolio optimization model was utilized to estimate the weights of the assets in the portfolio. Log returns, skewness, kurtosis, Shapiro-Wilk, and ADF were measured to describe stationarity and normality of the data. Data autocorrelation was assessed using ACF and PACF for volatility before ARIMA-GARCH modeling. All methodology was followed by appropriate hypothesis tests. Finally, 5-dimensional copulas were used for the VaR estimations for different confidence intervals. While AIC and BIC showed that t-copula was the best fit, the Clayton copula passed the goodness-of-fit test with the largest p-value. Subsequently, the Clayton copula generated VaR estimations closest to the historical data. The method used in this study can be extended for more than five assets without theoretical obstacles
MEDICAL IMAGE CLASSIFICATION USING ALGORITHM SELECTION
No full textAccurate medical diagnosis is a significant part of patient treatment. With the emergence of artificial intelligence, the process of medical diagnosis became easier. The most advanced and state-of-the-art models are based on large datasets, which increases the demand for memory and computational resources. Thus, the automated classification and detection of medical images can be a difficult problem due to small data availability. The issue of data scarcity can be addressed through meta-learning, which is known as "learning-to-learn" concept, that leverages both existing data and accumulated prior knowledge by automatically selecting the machine learning algorithms for unseen tasks. This approach has been widely used in classification tasks. This study propose a different approach of model selection based on priority orders of the algorithms. The method uses shallow classifiers to train different tasks and to select the best algorithm for new tasks. The priority based meta-learning demonstrates the potential to enhance classification performance in a cost-effective manner. The proposed method has outperformed the existing state-of-the-art achieving 100\% accuracy on a test set on meningioma classification and 98.3\% accuracy on test set on an adenocarcinoma classification
THE EFFECT OF PRODUCTIVE CAPACITIES ON MINING INDUSTRY OF KAZAKHSTAN
No full textThe thesis examines the relationship between Productive Capacities and mineral output in Kazakhstan, a country noted for its richness in a variety of mineral resources. As the demand for minerals continues to rise, it has been of particular interest to develop new mining sites. The abundance of deposits around the world has created competition among extractive countries, with the mineral potential becoming not the only decisive factor in investment decisions. The study identifies productive capacities that influence the mining output, hence, increasing the opportunity for further investments and development.
The thesis identifies several significant variables in relation to annual mining output in Kazakhstan, including energy, transport, productive capacity index (PCI), private sector, and ICT. The findings underscore the importance of these factors in driving the mining industry’s productivity and output levels. By understanding the impact of these factors, Kazakhstan can potentially attract more mineral investments, develop new mining sites, and boost its economic state and employment rates