The accident at the Chornobyl Nuclear Power Plant (ChNPP) in Ukraine in 1986 became one of the largest technological disasters in human history. During the accident cleanup, a special protective structure called the Shelter Object was built to isolate the destroyed reactor from the environment. However, the planned operational lifespan of the Shelter Object was only 30 years. Therefore, with the assistance of the international community, a new protective structure called the New Safe Confinement (NSC) was constructed and put into operation in 2019. The NSC is a large and complex system that relies on a significant number of various tools and subsystems to function. Due to temperature fluctuations and the influence of wind, hydraulic processes occur within the NSC, which can lead to the release of radioactive aerosols into the environment. The personnel of the NSC prevents these leaks, including through ventilation management. Considering the long planned operational term of the NSC, the development and improvement of information technologies for its process automation is a relevant task. The purpose of this paper is to develop a method for managing the ventilation system of the NSC based on neuro-fuzzy networks. An investigation of the current state of ventilation control in the NSC has been conducted, and automation tools for the process have been proposed. Using an adaptive neuro-fuzzy inference system (ANFIS) and statistical data on the NSC's operation, neuro-fuzzy models have been formed, which allows to calculate the expenses of the ventilation system using the Takagi-Sugeno method. The verification of the proposed approaches on a test data sample demonstrated sufficiently high accuracy of the calculations, confirming the potential practical utility in decision-making regarding NSC’s ventilation management. The results of this paper can be useful in the development of digital twins of the NSC for process management and personnel training.

The pilot study is devoted to the assessment of both the accumulation and spatial distribution of microplastics in the snow cover of the Zhetysu region. The height of snow cover in the study area varied from 4.0 to 80.5 cm, with a volume of melt water ranging from 1.5 to 143 L. The analysis of 53 snow samples taken at different altitudes (from 350 to 1500 m above sea level) showed the presence of microplastics in 92.6% of samples in concentrations from 1 to 12 particles per square meter. In total, 170 microplastic particles were identified. The main polymers identified by Raman spectroscopy were polyethylene (PE), polypropylene (PP), and polystyrene (PS). These are typical components of plastic waste. The spatial distribution of microplastics showed elevated concentrations near settlements and roads. Notable contaminations were also recorded in remote mountainous areas, confirming the significant role of long-range atmospheric transport. Particles smaller than 0.5 mm dominated, having high aerodynamic mobility and capable of long-range atmospheric transport. Quantitative and qualitative characteristics of microplastics in snow cover have been realized for the first time both in Kazakhstan and in the Central Asian region, which contributes to the formation of primary ideas and future approaches about microplastic pollution in continental inland regions. The obtained results demonstrate the importance of atmospheric transport in the distribution of microplastics. They indicate the need for further monitoring and microplastic pollution analyses in Central Asia, taking into account its detection even in hard-to-reach and remote areas. © 2025 by the authors.

This study investigates photocatalytic cells based on cocatalyst-loaded SrTiO3:Al and nano-SiO2 as a porous binder, immobilized on frosted glass. Comprehensive analysis confirmed the successful incorporation of aluminum into SrTiO3, increasing oxygen vacancy concentration and enhancing charge transfer. The deposition of RhCr2O3 and CoOOH cocatalysts significantly improved photocatalytic activity, boosting hydrogen and oxygen evolution rates to 3.8401 and 1.6319 mmol g−1 h−1, respectively. The introduction of nano-SiO2 increased hardness (0.23–0.25 GPa) and Young’s modulus (5.27–5.40 GPa), reinforcing structural integrity. The development of efficient photocatalytic panels requires a multifaceted strategy that considers chemical, mechanical, and optical properties together with stability, durability, and energy efficiency. Future research should focus on optimizing these key parameters to enhance system performance for industrial applications.

Thin transparent films of SnO2 were obtained from aqueous–alcohol solutions of SnCl4 on a flexible polyethylene terephthalate (PET) substrate by spray pyrolysis at 100 °C. The influence of the addition of aqueous ammonia to the film-forming solution on the different properties has been studied. Properties studied include surface morphology, phase composition and transparency of the formed films and the crystallization processes and band gap of the film material. It was found that the addition of aqueous ammonia causes the formation of skeletal crystals (NH4)2[SnCl6] with a perovskite structure in the film structure. The resulting films are promising for use in the technology of manufacturing flexible solar cells. © 2024 by the authors.

With the increasing need for digitalization in mining production, the relevance of applying modern geodesy and surveying technologies, particularly using digital communication systems and satellite navigation, has significantly increased. Scientific and technical progress has enabled the development and implementation of high-precision measurement technologies that significantly surpass traditional methods in performance and accuracy. A differential correction base station utilizing GNSS (Global Navigation Satellite Systems) data for measurements is created as a part of the geodetic work automation at Kacharsk deposit. This article presents the development of a software and a technical facility for a high-precision satellite positioning system, which has successfully passed all testing stages and has been implemented in industrial operation. This integrated system allows measurement tasks to be performed in real-time and post-processing modes, taking into account the complex conditions of signal transmission at the depths of quarries and beyond the dumps. The work was conducted by the D.A. Kunayev Mining Institute in collaboration with the Institute of Space Technique and Technology. The project co-financing by a private partner JSC "SSGPO". The development includes creating a differential correction center, which facilitates the transmission of correction information and differential corrections to mobile devices at the site. This provides the increased measurement accuracy and optimized production management processes in the constantly changing geometry of the quarry. A software-based mathematical algorithm for processing and analyzing satellite data has been developed within the project, significantly enhancing the efficiency of geodetic measurements. The implementation of satellite technologies not only improves the accuracy and efficiency of geodetic works but also promotes the digital transformation of the entire production process at Kacharsk deposit. Such developments are a key element in the strategy of creating an "intelligent mine", where all processes are maximally automated and optimized to ensure safety, efficiency and sustainability of production. In the context of sustainable development, the adoption of these technologies also contributes to minimizing the environmental impact of mineral extraction through precise positioning and planning of mining, leading to reduced waste and optimized resource usage. Financing and support for such projects highlight the importance of integrating science and technology into the sustainable development of the industry.

The concluding chapter provides a guiding map for successful R&Damp;I in the creative industries. After reiterating the book's vision and the multiple approaches deployed for analysing R&Damp;I in the creative industries, the chapter draws the most important conclusions for each book chapter. In doing so it stresses the importance of understanding the historical context in which the concept of R&Damp;I developed in the creative industries, the relevance of an extended definition of R&D that aligns with real mechanisms of the sector, the need for developing novel R&D-based typologies of innovation and support mechanisms (e.g. funding) that meet the needs of R&D implementers and the context within which they operate. The chapter then uses these reflections to draw overall conclusions about the frameworks, models and pathways resulting from the research. It finally closes by stressing: (1) the need for expanding this research (geographically and contentwise), (2) the real social, economic and cultural value that R&D brings to creative economies and (3) the importance of innovative funding and support ecosystems that are aligned to the realities of the creative sector.

Introduction. The article presents the results of the study of catastrophic water-ice flows that occurred on the Esentai River on 13.12.2022 and 16.12.2023. They had high flow speeds (4–5 m/s) and differed from slush ice drifts on lowland rivers. These flows resembled debris flows, because new material was involved and an avalanche-like increase of the water-ice mixture occurred. The flow movement was wave-like. These flows caused material damage. A similar phenomenon that occurred on January 6, 2006 on the Uzyn Kargaly River in Ile Alatau resulted in casualties. The main objective is to study the hydrological and meteorological conditions of the formation of water-ice flows on mountain rivers. Research methods. For this, the machine learning algorithm “Random Forest” was used. This is a method of automatically clustering data and dividing it into similar groups. It allows one to build “Decision Trees” that separate the meteorological situation and determine threshold values for each group. The results of the research. For the Central Asian climate, cooling down to 20–25 degrees below zero is an anomalous phenomenon. This causes the accumulation of anchor ice and a significant increase in the water level in the channel: 1–2 m above the winter low-water level. If cooling is replaced by a sharp warming with positive temperatures, destruction of ice structures and formation of ice-jam floods occur. Discussion. The difficulty in using this algorithm lies in interpreting the data and comparing it with already known results, since some machine “Classification Trees” take into account only general climate features. But certain threshold values are already confirmed by other scientific researches. This means that the algorithm works and produces results with a minimal statistical error. Resume. Threshold values of meteorological elements on the day of ice-jam floods were determined: daily warming gradient was more than 3.3°C/day, air temperature anomaly was below 2.6 °C, the sum of air temperatures over the previous 5 days was 42.5°C. They can be used to predict dangerous situations on mountain rivers. © 2025, North Caucasian Institute of Mining and Metallurgy, State Technological University. All rights reserved.

In the context of continental climate conditions like those in Kazakhstan, ground source heat pump heating systems are regarded as highly effective solutions for transitioning to cleaner and greener heating methods. This study undertook both experimental and theoretical investigations to develop a ground source heat pump-based heating system tailored to Kazakhstan's weather conditions and to assess its thermodynamic performance. The system, utilizing a water-to-water heat pump integrated with a ground source heat exchanger and R134a refrigerant, was designed to provide hot water for space heating needs. The results showed a close agreement, within 6.2%, between the predicted and experimental coefficient of performance values. Additionally, the study explored the use of environmentally friendly refrigerants, including R152a, R450A, R513A, R1234yf, and R1234ze, as potential replacements for R134a. While R152a exhibited promising performance in terms of coefficient of performance, its flammability raised safety concerns. The heating system employing R450A, R513A, R1234yf, and R1234ze displayed slightly lower coefficient of performance values (2-3%) compared to R134a. The analysis revealed that the compressor was the primary source of exergy destruction, followed by the expansion valve, evaporator, and condenser. Given their low flammability and reduced environmental impact, R450A, R513A, R1234yf, and R1234ze emerged as valuable alternatives to R134a. © 2024 American Institute of Physics Inc.. All rights reserved.

Starch-based biopolymers derived from renewable resources offer a sustainable alternative to plastic packaging. One of the main advantages of starch-based biopolymers is their ability to biodegrade, as well as being economical due to the availability and low cost of starch. This makes them more economically attractive for producers and consumers. Although there are still problems with improving their properties. This research centered on developing a biodegradable biofilm from oxidized corn starch and carboxymethylcellulose, using succinic anhydride as a crosslinker. The biofilm’s mechanical strength, water absorption, and biodegradability were evaluated and compared to a commercial biopolymer. The biofilm exhibited a strength of 0.78 MPa, absorbed 0.21% water, and had a biodegradability rate of 0.008%. These findings suggest that the biofilm has significant potential for industrial applications, particularly in the biofilms and bioplastics sector. This study contributes to the ongoing global efforts to create sustainable alternatives to conventional plastic packaging, a critical aspect of environmental preservation. The promising characteristics of the synthesized biofilm indicate its potential to significantly influence the future of packaging materials. This research marks a progressive step in the pursuit of sustainable packaging solutions. © 2024, al-Farabi Kazakh State National University. All rights reserved.

Credit risk assessment is an essential element of financial stability, impacting banking institutions, governmental financial entities, and individual creditworthiness. This paper presents a machine learning-based credit score model aimed at improving the precision of financial risk evaluations through the use of predictive analytics. The model employs sophisticated algorithms, including Random Forest, XGBoost, and Neural Networks, to categorize individuals into credit risk classifications based on their transaction histories, loan repayment patterns, and income stability. Explainable AI methodologies, such as Local Interpretable Model-Agnostic Explanations (LIME) and Shapley Additive Explanations (SHAP), enhance transparency in decision-making, hence augmenting trust in credit assessments. The research analyzes the effects of economic volatility and regulatory modifications on credit risk evaluation, specifically in Kazakhstan, where there has been a notable increase in consumer loans. Empirical study and thorough experimentation reveal that machine learning models surpass traditional statistical methods, enhancing predicted accuracy and reducing biases in credit assessment. The proposed approach improves financial inclusion by offering a scalable and transparent method for real-time evaluations of creditworthiness. This research advances the creation of adaptable and robust credit risk models that respond to changing financial environments.