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IMPROVED HUMAN POSE ESTIMATION USING SYNTHETIC DATA
No full textHuman Pose Estimation (HPE) is a cornerstone in the progress of computer vision, with the YOLOv8 algorithm emerging as a leading framework due to its remarkable performance. This study concentrates on improving YOLOv8’s accuracy and generalizability by integrating synthetic data into its training process. Utilizing Nvidia Omniverse, known for producing highly realistic synthetic environments, we crafted a dataset tailored for HPE enhancement. The integration of this synthetic dataset aimed to sharpen the precision of YOLOv8, broadening the scope of its applicability. Results showed a significant improvement in model performance, with an up to 19% increase in mean Average Precision (mAP) at 0.5 IOU and up to 12% rise in mAP across the 0.5-0.95 IOU range compared to models trained on conventional datasets. These findings highlight the potential of synthetic data to augment real-world data collection and underscore its value in developing more robust and precise HPE models, encouraging a shift towards innovative training approaches in computer vision
CHARACTERIZATION OF PHYTOPLANKTON COMMUNITIES USING IMAGING FLOW CYTOMETRY ALONG THE SALINITY GRADIENT IN TENGIZ-KORGALZHYN AND ARAL LAKE SYSTEMS OF KAZAKHSTAN
No full textTwo Kazakhstani saline lake systems were studied in the thesis: the Tengiz-Korgalzhyn Lake System and the Aral Lake System. The Tengiz-Korgalzhyn wetlands, lying on the main migratory routes of transitional birds, where more than a hundred protected and endemic species inhabit, are one of the important wetlands in Kazakhstan and Central Asia. In 1976, it was included in the Ramsar Convention's list and 2008 in the UNESCO World Heritage List. The second system is the Aral Lake System, initially the Aral Sea, which saw a significant shrinkage due to environmental and anthropogenic (water withdrawal, increase of agriculture) reasons in the last 60 years, which led to segregation and disappearance of some parts of it. Both systems provide strong salinity gradients, which are represented in biotic communities, specifically phytoplankton. Planktonic indicators are important for lakes ecosystem health evaluation.
To accomplish this, the water samples were collected from both lake systems with the instant measurements of physico-chemical parameters. The samples were then analyzed using imaging flow cytometry (IFC) for further phytoplankton classification and counting. The phytoplankton metrics were obtained and statistically analyzed, and diversity index calculations, and canonical correspondence analysis (CCA) were performed. According to the IFC results, Large and Small Tengiz Lake, which had a narrow range of all environmental parameters, was characterized by a homogenous community composition in all the sites with similar phytoplankton FlowCam-based group proportions. In contrast, significant trends might be seen in changes in phytoplankton groups in small lakes of the Tengiz-Korgalzhyn Lake System that showed a wide range of salinity. In the Tengiz-Korgalzhyn Lake System, the highest phytoplankton abundance was observed mainly in the less saline waters (oligo- and mesosaline sites) and the most hypersaline areas, with the decline in total abundance in the in-between salinity zones. However, the number of phytoplankton groups saw a decline as the salinity rose. The same trend for overall phytoplankton abundance was observed in the Aral Lake System, which has a strong salinity gradient as well. In addition, the trends of phytoplankton at different salinity levels that promote the dominance of various phytoplankton groups in the lake systems were described. Moreover, it was found that along salinity gradients, most phytoplankton groups differed in response to environmental parameters between these lake systems.
The significance of this research is the contribution to phytoplankton studies in two Kazakhstani lake systems and the development of classification systems for imaging analysis of phytoplanktpn suitable for IFC. These classification systems can serve as a basis for further seasonal studies of the lakes and for the development of artificial intelligence algorithms. In addition, this study may also contribute to the knowledge of algae in inland saline lakes
FACTORS INFLUENCING FAMILY LANGUAGE POLICY OF AMERICAN UZBEK FAMILIES IN KAZAKHSTAN
No full textFactors Influencing Family Language Policy of American Uzbek Families in Kazakhstan
Kazakhstan’s economic development has attracted many migrants and expats who choose it as a destination for work and a place to reside. While embracing Kazakhstan’s cultural diversity, these families often face significant challenges maintaining their ethnic language and culture. This happens particularly in multilingual environments where a hierarchy of languages impacts the family language policy of these families. Given the lack of studies on expatriate families’ language planning and strategies for preserving their ethnic languages and the limited studies on FLP in Kazakhstan, this research is crucial to filling the research gaps. It is important to explore the factors that shape the Family Language Policy of expatriate and intermarriage families and the strategies they employ to maintain their ethnic languages in the multilingual context of Kazakhstan. This study explored the factors influencing the Family Language Policy of American Uzbek families residing in Kazakhstan. It provides insights into how these families maintain their ethnic languages and cultures in Kazakhstan’s predominantly Kazakh and Russian-speaking context. The study employed a mini-ethnographic case study design, and data was collected using written narratives, non-participant observations, and semi-structured interviews. The participants were two expat families residing in Kazakhstan. The findings reveal that both external and internal factors shape the family language policy of American Uzbek families in Kazakhstan. These families use strategies to preserve their ethnic languages and culture, including the help of diaspora communities, grandparents’ support, digital tools, and keeping their national dishes. The study shows the crucial role of FLP in preventing language and culture loss among American Uzbek families in Kazakhstan. These findings provide crucial implications for policymakers. By shedding light on the various strategies to prevent language and culture loss, this study provides a roadmap for policies to promote a multilingual, inclusive society and preserve diverse languages and cultures
CAMERA VIEW-INVARIANT MULTI-PERSON 2D HUMAN POSE ESTIMATION FOR DEPTH AND FISHEYE CAMERAS
No full text"This world is gonna be saved by science and art, as well as curious minds and golden
hands, and open hearts...
Z, 2024
Human pose estimation is the automatic location of the human body parts from
an image or video. It is considered a prerequisite for tasks such as activity recognition
and human tracking and found its application in human-computer interaction, virtual
reality, and sign language recognition. Most of the research on human pose estimation
is concentrated on standard RGB cameras, while depth and fisheye cameras were given
much less attention due to the lack of corresponding datasets. Since fisheye and depth
cameras have several advantages over regular cameras, this project believes that it
is important to promote research on human pose estimation for these cameras and
proposes to create synthetic camera view-invariant multi-person depth and fisheye
image datasets for 2D human pose estimation. Furthermore, it is planned to train
state-of-the-art human pose estimation models on these datasets and check their
generalization on real images
SEWAGE SLUDGE VALORIZATION: BIOCHAR PRODUCTION FOR ENHANCED ADSORPTION OF CONTAMINANTS
No full textMunicipal sewage sludge (SS) from wastewater treatment has attracted much interest from the agriculture and energy sectors. However, concerns about harmful contaminants in sewage sludge limit its direct use in developed countries. Alternatively, a thermochemical conversion process such as pyrolysis of SS produces several value-added products, including bio-oil and a stabilized form of pyrolytic residuals with a large specific surface area and functional groups. This study examined the effects of pyrolysis temperature (500 oC, 650 oC, and 850 oC) and SS particle size (S1: 800 - 1000 µm, S2: 400 - 800 µm, S3: 100 - 400 µm, S4: ≤ 100 µm) on the derived biochar’s yield and its methylene blue and mercury adsorption capacity. The adsorption capacity and kinetics results were used to identify the best thermal treatment conditions for the SS. Irrespective of the particle size at a pyrolysis temperature of 500 oC, the SS-derived biochar followed the second-order adsorption kinetics. SS-derived biochar of 100 - 400 µm particle size was identified as a promising adsorbent due to its high adsorption capacity for methylene blue and a comparable initial adsorption and desorption rate constant based on the Elovich model. Further alkali treatment (Biochar: NaOH = 3:4) of 100 - 400 µm particle size enhanced the specific surface area of the biochar from 27.5 m2/g to 144.27 m2/g and increased the adsorption capacity of methylene blue and mercury to 35 mg/g and 36 mg/g respectively. These SS-based adsorbents are cost-effective and have a very high methylene blue and mercury adsorption capacity, serving as a beneficial modifier adsorbent. This advantage is poised to refine parameters in sewage sludge preparation before and after pyrolysis, offering a dependable material for engineering applications
OPTICAL AND PHOTOVOLTAIC PROPERTIES OF ORGANIC SOLAR CELLS VS BULK-HETEROJUNCTION MORPHOLOGY
No full textThe performance of organic bulk-heterojunction solar cells is closely tied to the morphology of their active layer, a disordered mixture of organic donor and acceptor materials, where photoexcitation and charge generation occur. In this thesis work, optical mixing principles are utilized to model the common active layer structure PM6:Y6. By controllably varying the size and shape of acceptor inclusions within a donor matrix, I investigate how changes in morphology affect the optical properties of the resulting effective medium. This finding is further utilized in transfer matrix optical simulations in combination with the Hecht equation to analyze the effect of morphology on the photovoltaic properties of organic solar cells. The reported findings can guide the optimal fabrication of similar structures by providing valuable insights into which characteristics of the blend could potentially hinder or benefit the performance of photovoltaics
INVESTIGATION OF ADDITIVELY MANUFACTURED TI-6AL-4V AND TI-6AL-4V-TA ALLOYS LATTICE FOR LOAD-BEARING IMPLANT APPLICATION
No full textBone implants have been a critical solution for addressing bone defects and disorders, with recent advancements in Additive Manufacturing (AM) allowing for creating customized implants using materials like Titanium and its alloys. This study focuses on lattice structures manufactured through Selective Laser Melting (SLM) technology, exploring the mechanical properties and biocompatibility of Titanium alloys, specifically Ti–6Al–4V and Ti–6Al–4V–Ta, for potential use in load-bearing applications. The lattice structures aim to mimic natural bone architecture, offering improved strength-to-weight ratios and promoting osseointegration.
A comparison between Ti–6Al–4V and Ti–6Al–4V-Ta results is conducted, covering mechanical analysis, powder characterization, electrochemical corrosion, and biomedical compatibility. The literature review highlights the increasing interest in Ti-Ta alloys, especially in lattice structures, with a scarcity of research in this area.
The methodology encompasses powder characterization, lattice structure design, SLM process parameters, post-processing, and detailed characterization techniques. The mechanical analysis involves compression, tensile testing, and hardness measurements. Morphological analysis and crack investigation contribute to a comprehensive understanding of lattice structures. An electrochemical corrosion test checks corrosion resistance, which is essential for medical implant applications. Biomedical compatibility is assessed through measurements for bacterial adhesion on SLM-printed surfaces.
The research aims to bridge the gap in understanding Ti–6Al–4V–Ta alloys manufactured with SLM, providing insights into their mechanical properties and potential applications in load-bearing implants. The outcomes of this study contribute to advancing the field of additive manufacturing for biomedical applications, offering valuable data for the development of safer and more effective bone implants
THE PROTECTIVE ROLE OF TEACHER OCCUPATIONAL WELL-BEING ON THEIR TURNOVER INTENTIONS IN SECONDARY SCHOOLS IN KAZAKHSTAN
No full textThis study uses data from TALIS 2018 to investigate the relationship between teachers’ occupational well-being and their turnover intentions along with factors influencing teacher well-being. Occupational well-being refers to teachers’ responses to the cognitive, emotional, health, and social conditions pertaining to their work and their profession. First, the findings revealed that Kazakhstani teachers report overall average levels of well-being, although strains on their physical and mental health were observed. Second, female, middle-age, and more educated teachers tend to report lower levels of well-being in the Kazakhstani context. Third, school climate and professional collaboration were identified as significant predictors of teacher occupational well-being. Finally, findings indicate that as teachers’ stress levels increase, their intentions to change schools or quit the teaching profession increase. These findings highlight the importance of addressing stressors within the teaching profession to support teacher retention and overall job satisfaction. The study provides implications for school administrators and policy-makers to improve the occupational well-being of teachers in secondary schools
MODELING COMPLEX RELATIONSHIPS IN GEOMETALLURGICAL VARIABLES: ENHANCING METHODS WITH ACCEPTANCE-REJECTION AND HIERARCHICAL GAUSSIAN CO-SIMULATION
No full textResource estimation is the basis of efficient and sustainable mining. Accurate mineral resource estimation is critical to optimizing mine planning, minimizing waste, and ensuring the economic viability of mining operations. Traditionally, resource estimation focused primarily on grade, the concentration of valuable minerals in ore bodies. However, the evolving complexity of modern mining requires a holistic approach that includes not only the grade but also the geometallurgical properties of the ore. Geometallurgical properties, which include attributes such as mineralogical composition, texture, and work index, play a key role in shaping mining operations. Understanding and modeling these properties is essential to unlocking the full potential of mineral deposits.
In the context of mining, geological complexity often leads to complex non-linear bivariate relationships between different attributes of ore bodies. These relationships can pose significant challenges for resource modeling, as traditional methods such as Principal Component Analysis (PCA) and Minimum/Maximum Autocorrelation Factor (MAF) are ill-suited to handle such complexities. These methods are inherently linear and may fail to capture the nuanced interactions and dependencies within geologic datasets.
This research paper presents a new approach to address the complexity of resource estimation in mining, particularly when dealing with non-linear bivariate relationships between geometallurgical properties. The proposed method combines the accept-reject method with hierarchical sequential Gaussian co-simulation. This approach enables the careful modeling of complex relationships (non-linearity) within geological data, leading to more accurate resource estimates and better-informed mining decisions.
A case study is presented in which the acceptance-rejection method with hierarchical sequential Gaussian co-simulation is applied to the modeling of two geometallurgical properties, recovery and chalcopyrite. The study shows how this innovative approach increases resource estimation accuracy by capturing non-linear dependencies and spatial variability between these two variables. Due to the hierarchical nature of geological data, the method adapts to different scales of variability, resulting in more realistic and practical resource models.
The findings of this research not only highlight the importance of integrating geometallurgical properties into resource estimation, but also provide a valuable solution for solving complex nonlinear bivariate relationships in geostatistical analysis of other regionalized variables. This approach has the potential to revolutionize resource modeling practices in the mining industry, leading to more sustainable, efficient, and economically viable mining operations. As mining continues to face evolving challenges and requirements, it is essential to incorporate advanced techniques such as the accept-reject method with hierarchical sequential Gaussian co-simulation to exploit the full potential of the Earth's mineral resources