This study describes both experimental and numerical investigations into the heat and mass transfer processes governing the vacuum freeze drying of camel milk, with a specific focus on improving the energy efficiency. A three-dimensional model was developed and solved using the finite element method to simulate temperature evolution and sublimation interface progression during drying. The numerical model was validated against experimental data, achieving strong agreement, with an 𝑅2 value of 0.94. A detailed parametric analysis examined the effects of the shelf temperature, sample thickness, and chamber pressure on the drying kinetics and energy input. The results indicate that optimising these parameters can significantly reduce the energy consumption and processing time while maintaining product quality. Notably, reducing the sample thickness to 4 mm shortened the drying time by up to 40% and reduced the specific energy consumption (SEC) from 358 to 149 kWh/kg. These findings offer valuable insights for the design of more energy-efficient freeze drying systems, with implications for sustainable milk powder production and industrial-scale process optimisation.

The ground source heat pump heating system is considered as one of the best solutions for the transition towards green heating under the continental climate conditions like Kazakhstan. In this paper, experimental and theoretical investigations were carried out to develop a ground source heat pump-based heating system under the weather conditions in Kazakhstan and to evaluate its thermodynamic performance. The water-to-water heat pump heating system, integrated with a ground source heat exchanger and used refrigerant R134a, was designed to provide hot water to meet the requirements for space heating. The predicted values of the coefficient of performance and the experimental results were found to be in good agreement within 6.2%. The thermodynamic performance of the system was also assessed using various environment-friendly refrigerants, such as R152a, R450A, R513A, R1234yf and R1234ze, as potential replacements for R134a. Although R152a is found to be a good alternative for R134a in terms of coefficient of performance and total equivalent warming impact, its flammability hinders its application. The heating system using refrigerants R450A, R513A, R1234yf and R1234ze shows 2–3% lower coefficient of performance than that of R134a. The highest exergy destruction is found to be attributed to the compressor, followed by the expansion valve, evaporator, and condenser. Considering their low flammability and low environmental impact, R450A, R513A, R1234yf and R1234ze are identified as valuable replacements for R134a. © 2022 by the authors.

Developing multifunctional flame retardants (FRs) has become a strategy to reply on needs for advanced polymers. Self-healing polymers are an emerging class of advanced polymeric materials, which have been upgraded progressively, and recently have taken the advantage of fire safety. Correspondingly, diverse industries like aerospace, automotive, construction and consumer electronics are benefited from flame-retardant self-healing polymeric materials, which underlines their increasing contribution to modern technologies. The self-healing characteristics stem from intricate chemical and physical interactions, adopting self-directed repair mechanisms leading to eliminating the need for frequent replacements, subsequently lowering maintenance costs and environmental impact. This review summarizes advantages of self-healing polymers with emphasis on exploring highly innovative advancements among bio-based hydrogels, aerogels, coatings, thin films, lithium-ion batteries and advanced ionotronic skin (-i-skin) structures embedding sensing features for smoke detection and flame exposure warnings, further broadening their application in smart technologies and safety-critical infrastructure. The outcomes of reports outline challenges remaining in developing such multifaceted materials in view of lack of information due to limited or exclusive investigations. However, further research may facilitate exploring dehydration, thermal shielding, and free radical quenching mechanisms contributing to flame retardancy performance of flame-retardant self-healing polymers. Sustainability and circular economy requirements are briefly discussed, in addition to outlining remarks on future developments. © 2024 Wiley Periodicals LLC.

This study analyzes the relationship between gold and oil prices and the stock market returns of Kazakh energy companies during and after the COVID-19 pandemic. We considered the period between 01.01.2020 and 31.12.2021 as the pandemic period and the period between 01.01.2022 and 31.03.2023 as the post-pandemic period. Then we performed a Granger causality analysis to identify the effect of gold and oil market returns on assets traded in the stock exchange. The data was retrieved from the website https://www.marketwatch.com/. Our findings are important in terms of proving the existence of an interaction between the Kazakhstan stock market and international markets. We have found that the gold prices had a causal effect on KZAP both in the pandemic period and in the post-pandemic period, while the oil prices had a causal effect on KZAP only during the pandemic period. We also found no causal effect of the international market prices on the KEGC and KZTO returns. However, this does not prove that there is no relationship between the international market returns and the returns of energy companies traded on the Kazakhstan stock exchange. The relationship between the international market returns and the returns of Kazakhstan stock market energy companies, if there is any, and whether it can be generalized to the long term can be analyzed by cointegration analysis and Vector Error correction model (VECM) methods. © 2023, Econjournals. All rights reserved.

The paper presents the results of laboratory studies on the removal of phosphate in a wastewater treatment plant by adding sludge formed at the water treatment plant (water treatment sludge—WTS) in the city of Astana (Kazakhstan). Raw WTS from the sludge drying beds was used in the study, and the content of chemical compounds present in the dry sludge residue was determined, yielding 10.8–14.6% aluminum oxide (Al2O3) and 4.58–5.31% iron oxide (Fe2O3). The sludge moisture ranged from 90.5 to 95.6%, and the ash content ranged from 51.3 to 63.9%. The raw sludge from the WTP was added to the wastewater collected before the sand trap and after biological treatment. On the basis of the obtained test results, it was found that the effect of phosphate removal depended primarily on the sludge dose and was above 90% when adding 50 mL of sludge to 1 L of sewage. To a lesser extent, the effect of phosphate removal was dependent on the contact time of the sludge with the wastewater and the place where the wastewater sample was taken. © 2023 by the authors.

Mineral resources, particularly copper, are crucial for the sustained economic growth of developing countries like Kazakhstan. Over the past four decades, the diversity and importance of critical minerals for high technology and environmental applications have increased dramatically. Today, copper is a critical metal due to its importance in electrification. Porphyry deposits are important sources of copper and other critical metals. Conventional exploration methods for mapping alteration zones as indicators of high-potential zones in porphyry deposits are often associated with increased cost, time and environmental concerns. Remote sensing imagery is a cutting-edge technology for the exploration of minerals at low cost and in short timeframes and without environmental damage. Kazakhstan hosts several large porphyry copper deposits, such as Aktogay, Aidarly, Bozshakol and Koksai, and has great potential for the discovery of new resources. However, the potential of these porphyry deposits has not yet been fully discovered using remote sensing technology. In this study, a remote sensing-based mineral exploration approach was developed to delineate hydrothermal alteration zones associated with Aktogay porphyry copper mineralization in eastern Kazakhstan using Landsat-8 and ASTER satellite sensors. A comprehensive suite of image processing techniques was used to analyze the two remote sensing datasets, including specialized band ratios (BRs), principal component analysis (PCA) and the Crosta method. The remote sensing results were validated against field data, including the spatial distribution of geological lineaments and petrographic analysis of the collected rock samples of alteration zones and ore mineralization. The results show that the ASTER data, especially when analyzed with specialized BRs and the Crosta method, effectively identified the main hydrothermal alteration zones, including potassic, propylitic, argillic and iron oxide zones, as indicators of potential zones of ore mineralization. The spatial orientation of these alteration zones with high lineament density supports their association with underlying mineralized zones and the spatial location of high-potential zones. This study highlights the high applicability of the remote sensing-based mineral exploration approach compared to traditional techniques and provides a rapid, cost-effective tool for early-stage exploration of porphyry copper systems in Kazakhstan. The results provide a solid framework for future detailed geological, geochemical and geophysical studies aimed at resource development of the Aktogay porphyry copper mineralization in eastern Kazakhstan. The results of this study underpin the effectiveness of remote sensing data for mineral exploration in geologically complex regions where limited geological information is available and provide a scalable approach for other developing countries worldwide. © 2025 by the authors.

The study analyzes anthropogenic disturbances of landscapes in Western Kazakhstan, which occupies 27% of the country’s territory. The main focus is on the impact of industry and agriculture, especially pasture use and the development of oil and gas fields. The application of remote sensing data and field surveys allowed us to identify the degree of landscape disturbance and to propose their classification into five levels of disturbance, from virtually undisturbed to severely disturbed. Cartographic analysis revealed that pastures occupy 53.83% of the territory, while industrial-technogenic impact accounts for 23.12%. This indicates a significant level of landscape transformation. The findings of this study can serve as a foundation for environmental monitoring and the formulation of recommendations aimed at reducing anthropogenic impacts. The study underscores the necessity for the sustainable management of natural resources in the context of industrial development in the region and provides crucial insights for maintaining ecological balance. © 2025 by the authors.

Microplastics (MP) have emerged as significant global pollutants, garnering considerable attention from both the scientific community and the public. The significance of microplastic research lies in its capacity to inform effective pollution control strategies and mitigate associated environmental and health risks. The review illuminated the complexities and challenges inherent in analyzing microplastics and nanoplastics within anthropogenically affected water systems. It underscored the necessity of standardizing methodologies to enhance the accuracy and comparability of research findings. The adoption of advanced analytical techniques represents a significant advancement in the field, paving the way for more effective pollution mitigation strategies. By adhering to this structured approach, the review aspires to contribute a comprehensive overview of current methodologies in microplastic research and to enrich the ongoing discourse surrounding environmental pollution and its far-reaching impacts. © Engineered Science Publisher LLC 2024.

This research paper explores the potential of deep learning techniques in the early detection of cardiovascular diseases using data collected from wearable devices. With the advent of advanced wearable technology, continuous monitoring of physiological parameters, such as heart rate, electrocardiogram (ECG) signals, and physical activity patterns, has become feasible. Leveraging the power of deep neural networks, we investigate the development of predictive models capable of identifying individuals at risk of cardiovascular diseases. The study employs rigorous data preprocessing techniques, encompassing feature extraction and data segmentation, in conjunction with Machine Learning algorithms to enhance model accuracy. Additionally, regularization methods, notably dropout, are harnessed to address challenges posed by diverse datasets. The research envisions the incorporation of data derived from everyday activities to comprehensively assess their impact on heart health, ultimately aiming to proactively alert individuals to deteriorating cardiovascular conditions. The findings presented herein encourage further exploration and innovation in the realm of cardiovascular disease detection, fostering advancements that can enhance public health and well-being. © 2024 IEEE.

With a power conversion efficiency (PCE) of more than 25%, perovskite solar cells (PSCs) have shown an immense potential application for solar energy conversion. Owing to lower manufacturing costs and facile processibility via printing techniques, PSCs can easily be scaled up to an industrial scale. The device performance of printed PSCs has been improving steadily with the development and optimization of the printing process for the device functional layers. Various kinds of SnO2 nanoparticle (NP) dispersion solutions including commercial ones are used to print the electron transport layer (ETL) of printed PSCs, and high processing temperatures are often required to obtain ETLs with optimum quality. This, however, limits the application of SnO2 ETLs in printed and flexible PSCs. In this work, the use of an alternative SnO2 dispersion solution based on SnO2 quantum dots (QDs) to fabricate ETLs of printed PSCs on flexible substrates is reported. A comparative analysis of the performance and properties of the obtained devices with the devices fabricated employing ETLs made with a commercial SnO2 NP dispersion solution is carried out. The ETLs made with SnO2 QDs are shown to improve the performance of devices by ∼11% on average compared to the ETLs made with SnO2 NPs. It is found that employing SnO2 QDs can reduce trap states in the perovskite layer and improve charge extraction in devices. © 2023 The Authors. Published by American Chemical Society.