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KAZAKHSTAN'S ENERGY TRANSITION
No full textA country with abundant coal, oil and natural gas resources, Kazakhstan was producing on average 2.3 times more energy than it needed domestically each year over the past two decades. Not surprisingly, more than 95% of the country’s domestic energy needs came from fossil fuels, primarily coal and, increasingly gas in recent years. This heavy reliance on fossil fuels meant that energy-related emissions are high – around 80% of
the country’s total greenhouse gas (GHG) emissions in 2020, excluding effects of land use change.
Amid the public controversy, Kazakhstan President Kassym-Jomart Tokayev announced that a national referendum would be held to decide the fate of the proposed nuclear power plant. Nuclear energy was only one prong of Kazakhstan’s energy transition strategy, but it underscored some of the challenges the country faced. For a country used to tapping its vast store of hydrocarbons, how could Kazakhstan decarbonise its energy value chain for a more sustainable future
PARENTS’ ATTITUDES TOWARDS COST-SHARING AND ITS IMPACT ON THE AFFORDABILITY OF HIGHER EDUCATION IN KAZAKHSTAN
No full textThe main purpose of this research is to explore and understand the attitudes of Kazakhstani parents regarding cost-sharing for higher education and to examine how this attitude affects the affordability of higher education for their children. The Cost-Sharing concept (Johnstone, 1986) is used as a conceptual framework to examine parents' attitude towards responsibility for the distribution of costs for higher education of their children in Kazakhstan. A quantitative correlational research design is conducted by using an online survey tool. The study sample included 209 parents of students in grades 8-11 in two schools in the city of Ust-Kamenogorsk. This research reveals parents' resistance toward cost-sharing concept and their preference for full government funding of their children's higher education. However, at the same time, there is a high level of concern about the future financial burden; parents note that they consider higher education in Kazakhstan affordable. This study has important implications for the government of Kazakhstan and its higher education institutions by further analyzing the impact of government initiatives on parental attitudes toward spending in higher education
THE LEGAL FRAMEWORK ON THE SITUATION OF STRAY ANIMALS IN KZ: PRACTICES AND POLICY RESPONSES
No full textThis research study explores the situation of stray animals in Kazakhstan, emphasizing their rights and the regulatory framework. It analyzes the law “On Responsible Treatment of Animals”, its effectiveness, and public perceptions. The study examines historical and social backgrounds, identifying normative gaps in legislation. It investigates Kazakhstan's regulatory framework regarding homeless animals, encompassing their rights, security, and punishment for violations. It also contemplates global practices applicable in Kazakhstan and characterizes the gap between current reality and government actions.
By applying a qualitative analysis which involves a survey and interviews, the study emphasizes public focus on safety issues over animal welfare problems. It discusses the regulatory environment, which leans toward reductionist approaches, and the financial difficulties hindering novel strategies. The study focuses on the need for a multifaceted and holistic approach, facilitating empathy and sense of responsibility towards stray animals. It invites legal reforms, public education, and evidence-based policymaking interventions to deal with the root reasons of stray animal growt
ANTIBODY GENERATION FOR DETECTION OF PHOSPHOGLYCEROYL PROTEIN ADDUCTS
No full textOne of the reactions of glycolysis, which is controlled by the enzyme GAPDH, results in the
synthesis of 1,3- bisphosphoglycerate. A highly reactive intermediate product known as
cyclic 1,3-phosphoglycerate (cPGA) can be formed from 1,3-bisphosphoglycerate. In
previous studies, it was shown that the knock-out of PARK7/DJ1 can cause an accumulation
of highly reactive cPGA and its adducts. However, the quantifiable methods to detect cPGA
adducts were lacking. We created a novel technique for the detection of cPGA modifications
with antibodies specifically recognizing cPGA-modified lysine and cysteine residues. We
developed a procedure for the generation of antigens modified by cPGA. We produced
antibodies that recognize lysine and cysteine residues modified by cPGA. Finally, we purified
these antibodies by negative selection with the help of affinity chromatography. Our results
demonstrated that purified antibodies can specifically recognise a diverse range of
cPGA-modified proteins and can be used for Western Blot analysis of cell extracts
CULTURAL SYMBOLISM OF FASHION BRANDS IN KAZAKHSTAN
No full textThis capstone project explores the cultural symbolism among the Kazakhstani fashion brands, particularly clothing and jewelry brands. The methodology of the study consists of the
individual interview, the focus group and digital ethnography. Data interpretation was done via open coding and thematic analysis. Findings show that the use of Kazakh cultural symbols is influenced by historical context, traditions, and cultural values. All of these led ethno-items to be trendy among society. Another key factor that facilitated the promotion of such items is influence of the businesses on social media platforms. They have an ability to influence and create new trends as well as to transmit cultural values. It depends on the brand identity and purpose. Thus, they are able to change customer behavior and attitudes. Analyzing the market and potential clients’ responses, it was determined that price, design and quality are priority factors when it comes to choosing a piece of clothing or jewelry. The following factor is the meaning of cultural symbols. Study participants also highlighted that through purchasing ethno-styled things, they feel belonging to the Kazakh community and build their own uniqueness among other people. Therefore, such symbols can be used as a tool of communication among individuals. Thus, it also helps to strengthen the sense of belonging, along with the growing trend of rethinking Kazakh culture
DEVELOPMENT OF INTEGRATED MEMBRANE BIOREACTOR AND CHEMICAL PROCESSES FOR ADVANCED WASTEWATER TREATMENT
No full textThe global rise in population and rapid industrialization and urbanization have resulted in a significant increase in wastewater production, putting a strain on existing treatment facilities. This has led to the release of certain pollutants into the aquatic environment, including emerging contaminants such as pharmaceuticals, pesticides, hygiene products, endocrine-disrupting agents, surfactants, and industrial chemicals. These pollutants are found in wastewater, groundwater, rivers and lakes and pose a potential threat to human health and cause various illnesses such as reproductive disorders, cardiovascular disorders, cancer, immune deficiency, nervous system syndrome, brain development delay, and memory disruption. The secondary treatment (biological processes) in a typical treatment plant is the most crucial step as it removes approximately 80-90% of pollutants. However, conventional treatment methods are not effective in completely removing emerging contaminants, and advanced treatment techniques must be utilized. In this work, different types of wastewaters containing emerging pollutants were treated using conventional activated sludge process, membrane filtration, and advanced oxidation processes.
The following emerging pollutants were used as targets: caffeine, ibuprofen, metronidazole, naproxen, sulfamethoxazole, bisphenol A, and carbamazepine.
The implementation of a sequencing batch reactor (SBR) led to the elimination of a significant amount (78-86%) of total organic carbon (TOC), with only small reductions (11%, 45%, and 6%) observed for naproxen, bisphenol A, and sulfamethoxazole, respectively. The SBR effluents then were treated with membrane filtration and chemical oxidation processes. Track-etch membranes (TEMs) and phase inversion membrane (PIM) were employed. It should be emphasized that TEMs were employed in wastewater treatment for the first time. TOC removal efficiency ranged from 1% to 6% for each of the four membranes evaluated. 10 nm TEM demonstrated almost complete removal of bisphenol A (93%) and insignificant removals for naproxen (11%) and sulfamethoxazole (14%). The elimination mechanism of bisphenol A employing membranes was probably connected to size exclusion and sorption. Ultimately, the effluents from SBR and membrane filtration were treated with sulfate-radical-based advanced oxidation processes (AOPs). Remarkably, full removal of emerging contaminants and TOC were obtained after 30 minutes for the effluents after membrane filtration using 10 mM of K2S2O8 and 25 mg/L of zero-valent iron (ZVI) under UV, demonstrating the great potential of combining membrane filtration and AOPs.
Pharmaceuticals commonly found in wastewater, such as caffeine, metronidazole, and ibuprofen, were investigated for removal using a combination of continuous flow-activated sludge process and advanced oxidation processes. The study found that ibuprofen and caffeine were completely degraded, while metronidazole was only partially degraded. However, the presence of ibuprofen and caffeine hindered the nitrification process, while metronidazole suppressed the activity of denitrifying microorganisms. Biological treatment resulted in complete degradation of ibuprofen and caffeine but only 56% degradation of metronidazole. Advanced oxidation processes using hydroxyl and sulfate radicals were then used to eliminate the remaining metronidazole. The study demonstrated the effectiveness of AOPs in treating effluents from biological treatment processes.
Moreover, AOPs were used to treat actual slaughterhouse wastewater. TiO2 photocatalysis resulted in a 44% decrease in TOC after 60 min, while UV/98 mM H2O2 led to a 74% reduction in TOC after 150 minutes. Adjusting the pH to 3 and introducing Fe2+ into the system increased TOC removal to 82.5% after 150 minutes. Combining 15 mM K2S2O8 and UV led to a TOC reduction of 85%. Persulfate oxidation was also applied for the first time to treat wastewater from a slaughterhouse. The study shows that UV/K2S2O8 may be used as an additional post-treatment technique after biological treatment for water discharge criteria fulfillment and wastewater reuse.
Additional experiments were carried out using sulfate radical-based AOPs. Real municipal wastewater was treated using the UV/K2S2O8/Fe2+ process. Response surface methodology (RSM) was utilized to improve the treatment process by investigating the impacts of four independent parameters on TOC, TC, and TN removal. RSM precisely established the most suitable parameters for complete TOC removal, leading to total TOC mineralization at pH of 7.7, 30 mM K2S2O8, and K2S2O8 to Fe2+ ratio of 7.5 after 106 min. Attempts to use statistical models to determine optimum conditions for complete TC and TN elimination were, however, unproductive.
In addition, for the first time, a continuous flow UV/K2S2O8/ZVI system was put into operation for wastewater treatment. The RSM was used to study the impacts of the following process factors on TOC reduction: space time, the concentration of K2S2O8, and the K2S2O8/ZVI molar ratio. Carbamazepine was spiked into both synthetic and actual municipal wastewater to investigate its fate during persulfate oxidation. In the case of synthetic wastewater, 71% TOC reduction and full elimination of carbamazepine were accomplished. In the case of real wastewater, 60% TOC removal and full carbamazepine elimination have been achieved. The complexity of real wastewater and the presence of radical-reducing agents may explain the difference in TOC removal with synthetic wastewater.
Finally, the UV/K2S2O8/Goethite process was tested using landfill leachate, a highly contaminated effluent. Sulfamethoxazole was injected into both synthetic (SLL) and real landfill leachate (RLL) and used to evaluate the best treatment parameters for TOC and sulfamethoxazole removal using RSM. After 4.7 h of using the UV/K2S2O8/Goethite system, 87% TOC and 100% sulfamethoxazole were removed from the RLL. In addition, air stripping at pH 11 for 3 hours was utilized to eliminate ammonia from the RLL. For the first time, our studies indicate the efficiency of the UV/K2S2O8/Goethite system in eliminating organic materials from landfill leachate.
To summarize, it was discovered that at certain concentrations, emerging pollutants block the activity of microorganisms, hence affecting the effectiveness of the activated sludge process. AOPs based on sulfate radicals are an efficient post-treatment process for the removal of emerging pollutants and TOC following the conventional biological treatment or membrane filtration. Different chemical species such as Fe2+, zero-valent iron, and goethite were used to activate persulfate or hydrogen peroxide under UV. The materials used were characterized using state-of-the-art characterization techniques. Overall, the integrated use of track-etch membrane bioreactor and advanced oxidation processes demonstrated significant efficiency in the elimination of emerging pollutants in wastewater treatment
DETECTION AND CLASSIFICATION OF SEASON CLOTHES USING ML ON THE EDGE
No full textThe creation of a machine learning (ML) model for the automatic identification of
different types of seasonal clothes was the main goal of our research. The objective was to
categorize into four main groups: winter clothes, trousers, long-sleeve, and short-sleeve
items. Our goal was to facilitate the precise and efficient identification of different kinds of
clothing products by utilizing Convolutional Neural Network (CNN) algorithms about their
seasonal properties. We collected a dataset of over 1000 photos that represented the four-goal
categories to train and evaluate our model. Carefully tagged images were used to enable
supervised learning. The model gained the ability to recognize patterns and characteristics
typical of various seasonal clothes through a process of training and validation, facilitating
precise categorization. After a long training and validation process, our CNN model
performed admirably in the classification of seasonal cloth. The classification accuracy of the
model was higher than 80%. This high accuracy demonstrates the effectiveness of our
method and its potential for useful use in real-world settings
Kutpe app
No full textIn a rapidly developing environment, such as a public space, the set of people's time is of vital
importance. In banks, hospitals and shopping centers - a fair share of time can be spent standing
in line. The Qutpe Customer Wait Time Manager is designed to revolutionize traditional queuing
practices in busy public places.
By digitalizing the queue management process and allowing users to virtually join a queue
and monitor their place in line in real-time status, the Qutpe app effectively reduces wait times
and ambiguities with queuing. Faced with uncertainty and long in many public spaces in Astana,
the team was inspired to create a queuing app using modern technologies and queuing theories to
eliminate uncertainty and increase the efficiency of service.
This report outlines the development of the Qutpe system, detailing the project
approachᅳincluding the algorithms and technologies used, like Django for backend
development and PostgreSQL for database management. The document also covers project
execution challenges and successes, evaluation methods, and potential future enhancements to
ensure the system's adaptability and long-term usability in Kazakhstan and potentially other
markets
PREDICTION OF CUSTOMER CHURN USING MACHINE LEARNING
No full textCompanies place great emphasis on Customer Relations Management, in particular the factors related to customer retention, and, conversely, the rate of "churn", the loss and replacement of customers. The prediction of churn is especially important, due to the economic advantages of retaining and satisfying existing customers over the costs of acquiring new ones. Despite a plethora of research dedicated to the topic, it remains a challenge for commercial enterprises to accurately predict customer churn.
Machine learning, and more recently deep learning, have emerged as effective tools for the analysis of client data to help identify relevant factors and predict rates of retention and churn. Commonly employed methods include Random Forest, Gradient Boosting, ANN, XGBoost, Decision Trees, Support Vector Machine, Adaptive DNN, and MLP hybrid classifiers.
In this study, we analyze multiple open-source customer datasets using machine learning methods, based on the literature. We carefully select datasets with varying characteristics and from different domains, and apply best-performing algorithms to predict customer churn. We have successfully replicated previously published work along with some variations described in the text
INVESTIGATION OF ADDITIVELY MANUFACTURED ALSI12 KELVIN LATTICE STRUCTURES
No full textAdditive Manufacturing (AM) has facilitated the production of parts with complex geometries. A particularly notable application of AM lies in the development of structured materials or lattice structures, where the mechanical attributes are dictated more by shape and design than by the material's microstructure itself. Lattice structures are crafted by designing the unit cell's topology in every direction of space. This in turn offers lightweight and customizable mechanical characteristics for applications in aerospace, biomedical, and automotive fields. Although research has been conducted on lattice structures made from materials such as Ti, AlSi12Mg, and Fe alloys, studies on lattices composed of AlSi12 alloys are somewhat limited. However, given their lower density compared to steel, AlSi12 alloys represent a potentially more economical alternative to Ti-based alloys. Despite this advantage, the production of lattice structures from AlSi12 alloys has not become widespread. This study is devoted to the mechanical properties of compression of lattice structures of AlSi12 alloy. It describes how these properties and energy absorption capabilities are affected by structure geometry, evaluates the impact of different thermal treatments on performance, and explores new lattice configurations with improved performance by combining Kelvin lattice and BCC lattice. A study was carried out on kelvin lattices with different strut diameters and unit cell sizes to evaluate their mechanical properties and energy absorption capacity. It has been found that non heat treated lattices have a significantly higher energy absorption capacity than those that have been heat treated. Among them, the as-built kelvin lattice combined with the BCC configuration showed the maximum energy absorption capacity measured at 416 MJ/m^3. In contrast, a heat-treated kelvin lattice with a unit cell size of 6 mm demonstrated a minimum capacitance of 9 MJ/m^3. The as-built lattice exhibits superior mechanical characteristics but demonstrates considerable brittleness. This results in the formation of a shear band during compression, leading to a separative failure of lattice. This problem is directly related to the microstructural composition of the alloy, which includes a fibrous network of silicon surrounding a delicate Al phase. In addition, it has been observed that heat treatment negatively affects the energy absorption ability of the lattices. The effect of heat treatment varies depending on various mechanical aspects such as yield stress, Young's modulus and plateau stress. Notably, heat treatment changes the stress-strain behavior from a stretch dominated response to a bending dominated response. Through microstructural research, it was observed that heat treatment leads to the formation of Si agglomerates, which increase the ductility of the lattices