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.

In open-pit mines, traditional truck dispatching systems typically make it hard to balance the demand for regular ore grade blending with the need for operating efficiency. Some people have suggested that intelligent, IoT-enabled systems could be a solution, but there is not always a thorough, controlled evaluation of how well they work compared to traditional baselines. This study fills in the gaps by creating a discrete-event simulation framework that lets you compare a traditional, proximity-based dispatching strategy with a new IoT-enabled method in a quantitative way. The suggested algorithm uses a closed-loop feedback system that works in real time to make judgments about how to blend ore dynamically, always bringing the stockpile back to the goal grade. We tested the two systems in the same way and looked at important performance indicators like grade consistency, manufacturing throughput, and equipment use. The simulation results show that the IoT-enabled system is much better since it can keep the target ore grade with great consistency and stability. These metallurgical improvements were accomplished with almost no effect on total production throughput (< 0.3% difference), and they made the workload of the excavator much more even, which means that operations are more sustainable. This study gives strong, quantitative proof that a smart, grade-aware dispatching system can improve both metallurgical quality and operational efficiency at the same time. The simulation framework shown here is a useful model for reducing risk and measuring the effects of IoT and AI technologies. This will help Mining 4.0 operations become more productive and sustainable.

This scientific work analyzes the research on data collection and transmission technologies for automatic control of high-frequency ozonators in water disinfection. The purpose of the study is to improve disinfection efficiency and optimize the system's operation by using IoT and artificial intelligence technologies in the control of ozonators. During the study, data collected by sensors were processed in real-time through an IoT system and analyzed using artificial intelligence. Results showed that the efficiency of ozonator control increased by 25%, and ozone consumption was reduced by 20%. The use of a PID controller allowed for a 15% reduction in the duration of the disinfection process. The application of sensor networks reduced system downtime by 10%, ensuring continuous operation. Service costs were reduced by up to 30%, improving economic efficiency. These results proved the effectiveness of integrating IoT and artificial intelligence technologies. Furthermore, this method offers an environmentally friendly solution. In conclusion, improving the automatic control systems of ozonators is a significant step towards enhancing the water disinfection process. © Published under licence by IOP Publishing Ltd.

Froth flotation remains one of the primary methods for mineral resource beneficiation. Despite its widespread use, the process’s overall efficiency depends heavily on accurate and timely control of its parameters, highlighting the need for improved automation technologies. Applied methods: This paper reviews current approaches to flotation process automation, focusing on smart technologies, real-time sensors, and predictive control systems. Particular attention is given to methods for pulp level regulation, airflow adjustment, reagent dosing, and pH monitoring. Additionally, the implementation of machine vision systems is considered as a complementary tool for flotation diagnostics and control. Main hypotheses and conclusions: The analysis of existing research underscores the limitations of traditional control strategies and supports the shift toward hybrid, adaptive, and model-free techniques. These emerging approaches provide greater flexibility and responsiveness to process fluctuations. The integration of advanced sensor technologies, visual data processing, and predictive modeling can notably enhance the stability of flotation operations and improve mineral recovery efficiency. Practical significance: The findings of this work offer a scientific foundation for developing innovative automation solutions tailored to flotation processes. These solutions are aimed at optimizing operational performance, reducing manual intervention, and lowering energy and reagent consumption. As such, the adoption of intelligent control systems presents a promising path toward more sustainable and cost-effective mineral processing in modern industry. © National Academy of Sciences of the Republic of Kazakhstan, 2025.

Hydrogen production from biomass has emerged as a promising renewable energy solution. However, significant challenges such as thermodynamic inefficiencies, high raw material costs, low hydrogen molar yields, and difficulties in using lignocellulosic feedstocks hinder its large-scale implementation. Conventional methods have not been able to effectively address these issues, which makes modern approaches, such as in silico strategies, essential. This review explores the role of computational models like genome-scale metabolic modeling, synthetic biology, and metabolic pathway reconstruction in overcoming these barriers. By utilizing vast genomic databases and advanced computational tools, researchers can optimize microbial systems, improve hydrogen yields, and design more efficient biohydrogen production processes. These in silico methods provide a pathway to enhance the efficiency of biomass processing and enable the development of scalable and sustainable hydrogen production technologies. The review highlights recent advancements and discusses the potential of in silico approaches to address key technological and economic limitations, paving the way for the future of biohydrogen energy.

We investigate the photochemical transformations caused by the continuous CO2 laser radiation (105−106 W/cm2) as well as by the pulsed CO2 laser irradiation with a pulse energy of 3–4 J, a pulse duration of 200 ns, and an effective diameter of the laser spot of 1 mm on the surface of zircon (ZrSiO4). We study the X-ray emission spectra and photoluminescence of the sample surface after the CO2 laser irradiation. We establish that the action of laser radiation in the infrared range on crystalline zircon leads to selective sublimation of Silicon (SimOn) complexes and find increase in the content of Zr atoms and a decrease of Si atoms on the surface of the irradiated samples. The analysis of X-ray emission spectra of samples after CO2 laser irradiation shows also that there is a redistribution of the intensities of the K and L components in the spectra of irradiated zircon. The processes of increase in the Zr content and decrease of the Si content at the irradiated samples and the redistribution of electrons over the K and L shells of the Zr atoms can be caused by the resonant breaking of the Si–O bonds induced by the CO2 laser action; this leads to the creation of long-lived Zirconium–Oxygen defect nanoclusters [Zr[·]+Zr]n+ and [Zr[·]+Si]n+ on the surface of zircon induced by laser irradiation, where symbol [·]+ means an Oxygen vacancy and n is a charge of nanoclusters. Arising of such nanoclusters is confirmed by the photoluminescence spectra of laser irradiated samples. These spectra demonstrate the intense line of complicated form at a region of 400 nm. The feature of Zirconium nanoclusters shows that, in the clusters, the Zirconium atoms stay in different electronic states. © Springer Science+Business Media, LLC, part of Springer Nature 2023.