Technology audit and production reserves

Research of problems and prospects of economic adaptation of military servicemen: sociological and statistical analysis

Galyna Nazarova — 2025-05-09

The object of research is the system of economic adaptation of military personnel returning to civilian life after participating in hostilities during the full-scale war in Ukraine. As a result of the conducted analysis and sociological research, several key deficiencies in the functioning of this system were identified, including: insufficient level of state support during the transition to civilian employment, low level of informational assistance, difficulties with job placement, and limited access to retraining and entrepreneurial development.

To identify and address these shortcomings, a comprehensive approach was applied, including statistical analysis of the labor market, regional analysis of job vacancies, study of veteran support initiatives, and a sociological survey of servicemen using an online questionnaire. These methods made it possible not only to detect weaknesses in the adaptation system but also to develop proposals for its improvement.

The study found that 58 % of military personnel are only partially prepared for reintegration into civilian life. The main reserves for improving adaptation include expanding access to training programs (noted by 48 % of respondents), promoting entrepreneurship (important to 34 %), and developing digital and creative skills (needed by 22 % and 28 %, respectively). The results also revealed a high potential for self-employment among veterans, with 26 % intending to start their own business, which could become a growth driver for regional economic activity.

The authors interpret the results by asserting that effective economic adaptation is achievable through the creation of an integrated institutional support model. This model should combine state policy, digital labor market tools, human capital development, and the utilization of veterans’ creative potential. The results of the sociological study of the needs of military personnel, presented in the article, give grounds to argue about the possibility of their involvement in entrepreneurial activity. Such an integrated system can ensure not only employment but also economic self-realization and sustainable reintegration of veterans into post-war society.

Determination of the influence of raw milk β-casein polymorphism on the efficiency of making cottage cheese

Volodymyr Ladyka — 2025-05-09

The positive functional features of A2 milk and the increase in the percentage of animals with the A2A2 genotype will contribute to expanding the choice of dairy products, in particular, cottage cheese. It is expected that determining the influence of the protein composition of raw milk on the quality and yield of cheese will allow for effective selection of dairy breeds of cows. The object of the study is the technological process of producing cottage cheese, produced by the classical acid method of coagulation of milk proteins from cows with different β-casein genotypes (A1A1, A1A2, A2A2). Subject of the study: physical and chemical characteristics of raw milk (A1A1, A1A2, A2A2); yield and quality of cottage cheese. It was experimentally established that the milk samples have a typical composition and comply with DSTU 3662:2018. The average dry matter content in milk from cows with the A1A1 genotype was 12.73 %, with the protein-to-fat ratio varying within 0.76–0.83. In raw material samples from animals with the A1A2 genotype, the average dry matter content was 12.72 %, and the protein-to-fat ratio was 0.66–0.68. For milk from cows with the A2A2 genotype, the average dry matter content was 13.14 %, and the protein-to-fat ratio was in the range of 0.62–0.82. A study of the quality indicators of cottage cheese samples showed that the genetic variation of β-casein does not affect the sensory properties of the final product. The moisture, protein, and fat contents in cheese from milk from cows with the A1A1 genotype were on average 72.27 %, 9.77 %, and 15.47 %, respectively. In samples of cheeses from cows’ milk with A1A2 genotype, the average moisture content was 67.17 %, protein – 18.30 %, fat – 14.37 %. For cheeses from cows’ milk with genotype A2A2, the average moisture content was 67.47 %, protein – 15.30 %, fat – 15.40 %. It was found that the efficiency of cheese production from cows’ milk with A2A2 genotype is the highest and on average is 141.26 %, which exceeds similar indicators for A1A1 milk by 13.18 % and A1A2 by 2.21 %.

Determining the capabilities of generative artificial intelligence tools to increase the efficiency of refactoring process

Andrii Tkachuk — 2025-04-17

The object of research is a source code refactoring facilitated and proctored by generative artificial intelligence tools. The paper is aimed at assessing their impact on refactoring quality while determining their practical applicability for improving software maintainability and efficiency.

The problem addressed in this research is the limitations of traditional rule-based refactoring tools, which require predefined rules and are often language-specific. Generative AI, with its advanced pattern recognition and adaptive learning capabilities, offers an alternative approach. However, its effectiveness in handling various refactoring tasks and its reliability remain undisclosed.

The research involved multiple experiments, where four AI tools – ChatGPT, Copilot, Gemini, and Claude – were tested on various refactoring tasks, including code smell detection, efficiency improvements, decoupling, and large-scale refactoring.

The results showed that Claude achieved the highest success rate (78,8 %), followed by ChatGPT (76,6 %), Copilot (72,8 %), and Gemini (61,8 %). While all tools demonstrated at least a basic understanding of refactoring principles, their effectiveness varied significantly depending on the complexity of the task. These results can be attributed to differences in model training, specialization, and underlying architectures. Models optimized for programming tasks performed better in structured code analysis, whereas more general-purpose models lacked depth in specific programming-related tasks.

The practical implications of this research highlight that while Generative AI tools can significantly aid in refactoring, human oversight remains essential. AI-assisted refactoring can enhance developer productivity, streamline software maintenance, and reduce technical debt, making it a valuable addition to modern software development workflows.

Development of an approach to chat-bot personalization with generative artificial intelligence when realize an online assistant

Olha Kryazhych — 2025-05-03

The object of research is the interaction in the “human – machine” system during the user's interaction with generative artificial intelligence. The relevance of the research topic is due to the need to provide assistance to users in a narrow professional topic. To implement the goal set in the work, a model of operator decomposition was developed using the “Goals, Objects, Methods, and Selection rules” GOMS technology, taking into account the multi-level cognitive functions of a person. For this purpose, microoperators were used, which are responsible for combining various actions to find an answer to a question. A model with the decomposition of the operator μ was developed, which is responsible for cognitive functions when creating a request during human interaction with a chatbot based on artificial intelligence. The work used interaction with the ChatGPT chatbot.

The proposed decomposition algorithm was used as the basis for the online assistant plugin. The implementation is made in JavaScript, which allows it to be used on any sites and portals. The main components of the plugin are the interface for entering a query, a multi-level search mechanism on the site and in connected specialized libraries. The API integration of the plugin with ChatGPT was implemented.

As a result of the work, a study was conducted to experimentally determine the values of action and movement operators that are related to human mental activity and algorithmized in the online assistant. According to the results of the experiment, it was taken into account that for a chatbot, queries using foreign language signs and symbols and queries in the user's usual natural language are equivalent. To communicate with ChatGPT using the plugin, it is necessary to adhere to uniqueness and clarity when forming narrowly professional queries. The result was obtained that when querying in natural language on a topic familiar to the user, the online assistant adapts to the requirements more slowly. But at the same time, the speed of finding an answer and its formulation is accelerated. The problem of personalizing the online assistant was solved. This became possible thanks to the analysis of user behavior through the detailing of the query by micro-operators in the GOMS model. This allows to personalize the online assistant without user registration, only based on its behavior when forming a request.

The proposed approach can be used to create online assistants for the implementation of highly specialized complex projects on web platforms.

Optimization of technological modes of cupola melting according to the criterion of maximum combustion temperature

Dmitriy Demin — 2025-05-07

The object of research is the combustion temperature in the cupola furnace. The problem under study was the complexity of predicting the temperature as a function of the control parameters of the melting.

In the study, the control parameters were selected as the temperature of the air heating blown into the tuyeres and the completeness of fuel combustion. Using orthogonal experimental planning, a mathematical model was constructed in the form of a second-order polynomial, which allowed to identify the patterns of influence of each control factor on the resulting value – the combustion temperature.

The resulting mathematical model allowed to find out that both input variables are significant. However, if the nature of the influence of the air heating temperature on the combustion temperature is linear, then the completeness of combustion affects nonlinearly. The accuracy of the model turned out to be satisfactory, because all experimental data fell within the confidence intervals with a confidence probability of P=0.99. This allows to state the possibility of using the constructed model to predict the combustion temperature within the planning area.

The ridge analysis of the response surface established that the theoretical maximum value of the combustion temperature at the boundary of the planning area is about 3000 °C. This corresponds to the values of the input variables T_air≈1120 °C and η₀≈82 %. However, due to the fact that ensuring the air heating temperature at the level of 1120 °C may encounter technical complexity of implementation, the following values of the input variables can be recommended: T_air=783–1060 °C, η₀=71–80. They provide combustion temperatures in the range of 2690–2980 °C, i. e. values close to the suboptimal one determined by the ridge analysis.

These data allow making adjustments to the melting process, including being used for further searching for optimal melting control. The obtained solutions can be used in iron foundry shops of industrial enterprises equipped with cupola furnaces.