The results obtained reflect the determination of the possibility of the stability enhancement of the strength and thermal parameters of zirconium-containing ceramic materials via the addition of a stabilizing additive in the form of Y2O3. According to the data obtained, a connection between phase transformations caused by the addition of the stabilizing Y2O3 additive and an increase in strength characteristics, the growth of which is also due to the formation of the dispersion hardening effect during grain size reduction, was established. It was found that the formation of the stabilized Zr(Y)O2 phase results in thermal conductivity reduction, while the formation of the perovskite phase YZrO3 has a positive effect on the thermophysical parameters of ceramics. It was determined that the change in phase composition associated with the formation of the tetragonal Zr(Y)O2 phase at low dopant concentrations leads to an increase in strength characteristics, but a decrease in thermal conductivity, while the formation of the YZrO3 phase in the structure contributes to a growth not only in strength, but also thermophysical parameters.

This article investigates the improvement of water-based polyacrylate paint and varnish coatings through the incorporation of mineral fillers and hydrated lime, followed by activation in a vortex layer apparatus. The study focuses on the use of silica-activated diatomite as a modified filler and evaluates its effects on key physical and mechanical properties, including porosity, adhesion, elasticity, and covering power. The results show that the addition of modified diatomite enhances water resistance and mechanical performance without negatively affecting the main operational characteristics of the coatings. These improvements are attributed to a reduction in the free volume of the composite structure and the formation of modified polymer boundary layers, making the developed composition a promising material for durable protective and decorative coatings.

The purpose of the study is to improve the methods of assessing the processing capacity of railway stations based on scientific observation data. The paper considers a large private station that provides transport services to port terminals when performing railway and water transportation. The research is performed based on methods of analysis of time series and theory of operation of railways. The study established the maximum permissible volume of work for the port station and also assessed the impact of the combination of diverse types of goods on the amount of its processing capacity. The scientific novelty of the work is to improve the methods of evaluating the processing capacity of railway stations based on the methods of statistical data analysis. Unlike the existing method, the method allows you to more accurately consider local features of stations. The practical value of the results obtained is that they allow to predict the work of the station in the conditions of accidental flow of port terminals requests for servicing and avoid overload. This enables the station management to develop more efficient strategies for managing its work.

The article is dedicated to advancing technologies in chaotic optical communication and investigating chaotic laser generation in measurement systems and instruments. It conducts an analysis of the informational potential of chaotic signals in the optical range and explores the feasibility of implementing chaotic dynamics in lasers. It is demonstrated that among the current challenges are precision and stable generation of chaotic laser modes, as well as precision and stable synchronization of chaotic laser generators. The definition of chaotic modes is proposed as a set of characteristic types of chaotic oscillations that are stable, distinguishable, and reproducible within an oscillatory system, along with a method for estimating the number of modes and their designation. A method for modulating pump energy using a Chua generator is suggested. Additionally, a model for precision control of chaotic laser generation is proposed, allowing for the generation of chaotic laser modes with the ability to measure and control laser emission parameters and chaotic dynamics based on monitoring small changes in control parameters and influencing factors.

Kazakhstan’s mining sector, a vital pillar of the national economy, generates significant volumes of waste. This waste has been found to hold considerable residual value, presenting a substantial opportunity for resource recovery and economic benefit. To unlock this value, establishing efficient reverse logistics operations is fundamental, as it enables the recovery, recycling, and reuse of materials in a cost-effective and sustainable manner. This paper introduces a conceptual optimization framework tailored to Kazakhstan’s mining industry to explore the feasibility of reverse supply chain processes. The implementation of strategies informed by this model can improve resource utilization, reduce environmental impact, and deliver long-term economic benefits. The study also identifies potential challenges to adoption and suggests pathways for further refinement of the model to adapt to the evolving needs of Kazakhstan’s mining sector. The model provides a robust analytical foundation to support discussions on developing a holistic strategy for waste management in the sector. It offers key insights into optimizing waste handling, advancing material recovery technologies, and promoting collaboration between public and private stakeholders. By aligning these insights with the regulatory and economic landscape of Kazakhstan, the model serves as a reference point to shape a broader national framework. The outcomes of this study contribute to the achievement of Sustainable Development Goals (SDGs) 9 and 12 by promoting industrial innovation, resource efficiency, and responsible production practices within Kazakhstan’s mining sector.

Coal remains one of the key raw materials used in the energy industry to generate electricity and heat. As a result, diagnostics of the combustion process is still an important topic of scientific research. Correct implementation of the process allows the emission of pollutants into the atmosphere to be kept at a compliant level. Therefore, it is important to conduct the process in a manner that will not exceed these standards. A preliminary analysis of the measurement signals was carried out, and signal predictions of flame intensity changes were determined using the autoregressive moving average (ARMA) model. Different fuzzy neural network architectures have been investigated. Binary and multi-class classifications of flame states were conducted. The best results were obtained from the ANFIS_grid partition model, producing an accuracy of 95.46% for binary classification and 79.08% for multi-class classification. The accuracy of the recognition of flame states and the high convergence of the determined predictions with measurement signals validate the application of the proposed approach in diagnosing or controlling the combustion process of pulverized coal and its mixtures with biomass. Expert decisions determine the range of acceptable states.

The article discusses the different factors that contribute to the success of youth entrepreneurship projects. The research conducted was a systematic review of various research articles that involved the use of keywords such as "youth entrepreneurship", "youth entrepreneurship success", and "entrepreneurial intention". The systematic review helped in categorizing the factors into major groups or areas of influence that affect the success of youth entrepreneurship. These factors have been categorized into five groups based on their role in the creation of such projects. The groups include young entrepreneurs, government, business environment, educational organizations, and the financial sector. Each group consists of 7 to 10 factors that impact the development and successful implementation of a young entrepreneur's project. Although the government, educational organizations, financial sector, and business environment are all important factors that affect young entrepreneurs, a recent review has shown that it is also important to study the personal characteristics of these individuals. This is because many of the traits that lead to success in entrepreneurship develop at a young age, before someone even begins their entrepreneurial journey. The article aims to build a list of important factors based on the group “young entrepreneurs “ and role in the project's creation. Using the fuzzy analytic hierarchy method, the article identifies the crucial factors for the development of youth entrepreneurship projects in the "Young Entrepreneur" group. These factors include cognitive capital, non-cognitive capital, human capital, entrepreneurial intentions, family support/help in running the business, and availability of a business plan.

The current competitive market condition in the oil industry is so concentrated on companies' budgets that they require methods to extract oil from wells at the lowest possible cost. All pumping units, new or old, require regular preventive maintenance and constant inspection, and with 30 percent of total oil production coming from sucker rod pumps, the cost of diagnostics must be reduced accordingly.This article focuses on the intelligent diagnostics of the rod and borehole pump for preventive maintenance and monitoring during the life cycle of the well. The use of artificial intelligence methods for predictive diagnostics of equipment condition solves problems without production stoppages and without additional interventions from outside.

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.

Accurate event extraction and analysis are vital for understanding trends and making informed decisions in various domains. This paper presents a concise rule-based approach for event extraction in the aerospace domain, aiming to enhance decision-making processes. The proposed methodology utilizes rule-based pattern-matching techniques to identify and extract events from textual data, such as news articles and research papers. By leveraging linguistic features and syntactic structures, the approach effectively captures event-related information. Additionally, an event map visualization technique is introduced to provide a comprehensive overview of the extracted events and their relationships. The proposed approach, integrated into the Aerospace Expert System (AES), offers a powerful tool for tracking and analyzing events in the aerospace industry. Evaluation results demonstrate the high precision and recall of the rule-based approach, enabling stakeholders to gain valuable insights and make informed decisions in the aerospace domain.