As the volume of data continues to surge, researchers are confronted with the challenge of extracting meaningful insights from this wealth of information. Despite rapid advancements in Natural Language Processing (NLP) techniques in the AI industry, there remain gaps and opportunities for further exploration, particularly in the realm of data recombination techniques and methods. This paper proposes a novel text recombination method to facilitate the generation of recombined words from a given text. The process commences with a ’stanza’ model, which identifies and compiles Named Entity Recognitions (NERs) into a list. These NERs are then cross-referenced with Wikipedia pages to retrieve relevant information, thereby enhancing entity understanding and analysis. The ensuing step involves preprocessing the output text from the previous stage, generating a list of unique words while eliminating stop words. This preprocessing stage serves to remove noise and focus on meaningful words, laying the groundwork for more effective clustering. To enable clustering, we employ vector embeddings, representing words in a 2-dimensional space, rendering them suitable for clustering techniques. Notably, the proposed method further enhances results by re-clustering words after applying K-Means, thereby identifying the most fitting candidate words for recombination. Comparatively, this method outperforms large language models (LLMs) due to its incorporation of NER information, utilization of Wikipedia pages, and effective preprocessing techniques. Unlike LLMs, which operate as resource-intensive black boxes on static data, this method benefits from real-time information access and knowledge base updates. Furthermore, each stage of the process is visualized to control the progress correctly. Thus, due to the plots of word clusterization, the proposed text recombination approach showed positive results.

The article presents methods for calculating the production function based on complex-valued economics. The uniqueness of the work lies in the fact that the production function is presented as a criterion for managing and forecasting heat supply. Data from the boiler plants of the heat supply company in Taldykurgan are used as initial data. As a result of the analysis, it was decided to use the production function of the complex argument, which will increase the number of process indicators and conduct a more complete analysis of the production of thermal energy.

In the near future, AI may move beyond its role as a mere tool to function as a creative agent—potentially even as a virtual student or professor—capable of generating original artworks and contributing to research leadership. However, it remains unclear whether educational institutions are adequately prepared for such a rapid integration of AI into educational and research processes. This question becomes particularly relevant in the context of the rapid advancement of large language models (LLMs) and generative artificial intelligence (GenAI), given their potential to transform both the landscape of scientific research and educational methodologies. This study, therefore, examines how educational institutions are responding to the integration of AI into research and education. Specifically, we analyzed the policies and guidelines regulating the use of GenAI in both general universities and art-focused institutions, and conducted a strategic review of institutional approaches, along with a content analysis of selected curricula related to GenAI implementation. Based on the analysis, we posit that current GenAI policies in higher education are largely reactive, unevenly implemented across regions and disciplines, and often fail to address research-specific use cases and the distinct challenges faced by artfocused institutions. This finding aligns with recent studies showing that institutions tend to conform to external regulatory, normative, and mimetic pressures in their adoption of GenAI, often prioritizing legitimacy and compliance over proactive strategic vision (Singh, 2024). From an educational perspective, researchers further argued that Bloom’s Taxonomy requires revision to address the cognitive, affective, and metacognitive demands of AIassisted learning, underscoring the need for institutional policies that not only regulate GenAI but also foster critical thinking, ethical reasoning, and iterative learning processes in higher education (Gonsalves, 2024).

This article presents a mathematical model in the form of static equations of dependencies of input and output flows based on the equations of material and heat balance for the purposes of operational planning and control of the complex technological complex of Vanyukov melting (PV). Dynamic characteristics are presented for the purpose of controlling the thermal regime based on the technology of the developed melting process with blowing from below. As a result of the study, the developed mathematical model for controlling the smelting process when calculating the material flows of the charge will allow tracking changes in the thermal state of the smelting (by the copper content in the matte). This model can quite well describe the dynamics of the state of the process, both when establishing the impacts aimed at increasing the heating of the furnace, and at reducing its heating. Based on the equations, a computer model based on the dynamic programming method in the MATLAB software package has been developed. The scientific novelty lies in the fact that for the first time, the structure of a mathematical model has been developed that describes the processes occurring in the over-tuyere zone and the sludge zone of the smelting products. © 2023, Institute of Metallurgy and Ore Beneficiation JSC. All rights reserved.

An experimental analysis was carried out on a multi-stage energy conversion system configured in a solar panel–converter–load structure. Multi-stage converters are operated according to fundamental energy transfer principles, enabling the conversion of solar radiation into usable electrical power for consumer applications. The performance of the system was evaluated under different load scenarios, with variations in efficiency and output voltage behavior analyzed. The experimental results indicate that the integration of multiple converter stages leads to enhanced productivity of photovoltaic power systems. These findings emphasize opportunities for the optimization of photovoltaic technology and the advancement of energy conversion designs intended for automotive applications. © 2025, Politechnika Lubelska. All rights reserved.

The main method of applying optical principles of operation of the system for diagnosing pathologies of biological tissue at an early stage is proposed to use the formation of moiré effects. The aim of the work is to create theoretical foundations for a method of early determination of the state of the structure of biological tissue of objects, which contributes to the intellectualization of diagnostic processes and exposure, in particular, to light electromagnetic fields. This improves the quality of equipping scientific experiments in biology and medicine with modern technologies. The proposed method of diagnosing the condition of objects allows its use in early diagnosis of pathologies and, as a result, in the treatment of a wide range of diseases, as well as, for example, postoperative scar structures, a number of dermatological diseases.

The developed method of optimal design of flexible optical networks takes into account various factors and goals to ensure efficient use of resources, high performance, scalability, and adaptability. A mathematical model is developed to determine the key parameters of the fiber optic linear network route. The topology of the route of a fiber-optic linear network based on the proposed matrix model is proposed. An algorithm for using the matrix model of parameters based on the proposed variant of the fiber-optic linear network route is developed. The application of a systematic approach using the proposed method will allow network designers to achieve optimal design of flexible optical networks that efficiently use resources, provide high performance, as well as scalability and adaptability necessary for future growth and technological progress.

This article discusses the process of copper smelting in a Vanyukov furnace, with special attention paid to maintaining the optimal melt temperature, which is a key factor in the stable operation of the furnace. The article presents the results of experimental studies conducted at the Balkhash Copper Smelter, as well as experimental statistical data on the input and output parameters of the automated process control system (APCS). To build a mathematical model of the process, the System Identification Toolbox package in the MATLAB environment was used. As part of the work, modeling was carried out using a PID controller, its parameters were optimized to minimize the mean square error of regulation. In addition, the effectiveness of a fuzzy controller was considered, which showed a decrease in the dynamic error and a smoother process.

A comprehensive study was conducted to develop a two-dimensional mathematical model for a thermal storage tank containing internal disk-shaped obstacles. This model, incorporating appropriate initial and boundary conditions, was solved using the built-in solvers of the licensed COMSOL Multiphysics 5.6 software. The COMSOL model demonstrated a maximum deviation of 2.2% from experimental results and even smaller discrepancies compared to ANSYS Fluent, validating its accuracy in describing the charging and discharging processes of a sensible heat storage tank with internal obstacles. Using this validated algorithm, numerical studies were performed to analyse temperature distribution and performance indicators for three distinct tank configurations. Among these configurations, the storage tank with a middle disk consistently exhibited superior performance. This tank achieved the highest mixing efficiency, as evidenced by smoother variations in the Richardson number and a more uniform temperature distribution. It also attained the highest capacity ratio (90.12%) and exergy efficiency (81.67%), indicating its effectiveness in heat retention and the quality of stored thermal energy. Furthermore, it demonstrated the highest charging efficiency at 67.51%, highlighting its ability to store incoming heat more effectively. These findings establish the tank with a middle disk as the most efficient configuration for thermal energy storage and uniform temperature distribution. © 2024 Al-Farabi Kazakh National University.

Abstract In this paper, a digital twin of the network of heating systems for smart cities is developed using the example of the city of Almaty. The study used machine learning algorithms to estimate future thermal energy consumption and develop thermodynamic formulas. This work offers a thorough and in-depth analysis of thermal energy consumption. In addition, the paper identifies the relationship between thermal energy consumption and ambient temperature, and wind uncertainty in certain urban areas using machine learning methods to predict thermal energy consumption. Using both training and regression models, this interdependence is revealed. The obtained forecasts provide useful information for studying the structure of heat consumption in Almaty and reducing heat losses by reducing overheating in the zones of heating networks. In addition, the study analyzes high-resolution spatial data collected from 385 homes and 62 heat transfer circuits located throughout the city during the heating season. The study examines the degree of relationship between the ambient temperature and the amount of heat energy used in the areas of Astana. A minor impact of wind speed is also estimated. These discoveries allow us to use machine learning algorithms to find the location of hot spots and inefficient zones with high losses.