SUSTAINABLE INNOVATIVE DEVELOPMENT: design and management

Contents

Ershova Anna Dmitrievna
DECISION-MAKING METHODS IN DIGITAL PROJECT MANAGEMENT ECOSYSTEMS USING ANALYTICS AND AI

Yushkin Vladislav Nikolaevich
COMPARATIVE ANALYSIS OF MATHEMATICAL MODELS OF RATING SYSTEMS AND THEIR APPLICATION IN MACRO-TOURNAMENT MANAGEMENT

Rozanova Anna Vyacheslavovna, Predein Nikita Sergeevich, Ivliev Trofim Alekseevich, Tolstov Avdey Tarasovich, Saif Mujahed Abdullah Hayel
A TOOL FOR NORMALIZING REGULATORY INFORMATION DATA USING A LARGE LANGUAGE MODEL

Petrosyan Anna Surenovna, Gorbatenkov Timofey Vladimirovich, Kolchanov Konstantin Viktorovich, Myagchenko Ilya Dmitrievich, Saif Mujahed Abdullah Hayel
AI-AGENT PROTOTYPE FOR THE EDUCATIONAL COURSE AS PART OF THE EDUCATIONAL AND MULTIPLE-COURSE

Ershova Anna Dmitrievna, Student, Department of Information Technologies and Control Systems, Ural Federal University named after the first President of Russia B.N. Yeltsin

Abstract

This paper presents a critical analysis of the limitations of two key decision support tools in digital project management ecosystems: heterogeneous data integration architectures and digital twin technology. Based on a systematic review of publications and documented case studies (2020–2025), we demonstrate that the primary cause of widespread implementation failures is not technological immaturity but a systemic gap between technological potential and organizational readiness. Four major barriers are identified: semantic gaps in integration, data lake degradation into “data swamps,” opacity of AI algorithms, and organizational resistance. Practice-oriented recommendations are provided for shifting implementation priorities from tools to data governance, model explainability, and organizational culture development.

KEYWORDS: digital ecosystem, project management, data architecture, project digital twin, systems integration, artificial intelligence, semantic integration, data quality.

Download article DECISION-MAKING METHODS IN DIGITAL PROJECT MANAGEMENT ECOSYSTEMS USING ANALYTICS AND AI

Yushkin Vladislav Nikolaevich, Candidate of Technical Sciences, Associate Professor, Head of the Department of Information Systems and Technologies, Volgograd State Agrarian University

Abstract

The paper presents a model for predicting the results of sports competitions based on the application of the Poisson distribution and adapted for the analysis of hockey matches. The model was developed as part of the management of organizational systems and is aimed at improving the accuracy of predictions of the outcomes of sporting events by using statistical data on past performances of teams. The main research tool is the probabilistic approach, which allows taking into account not only the quantitative indicators of goals scored and conceded by each team, but also factors such as home advantage, as well as the relative strength of the attack and defense of the competitors. The analysis was carried out on the basis of retrospective statistics of hockey matches, including data on the results of games played both on the home court and away. The developed model can be used as a decision support tool in the management of sports organizations, including tournament planning, rating formation, team performance analysis and preparation of strategic actions to increase competitiveness. The results obtained can be used in automated forecasting systems, as well as in the analytical departments of professional sports clubs and federations.

KEYWORDS: forecasting model, organizational systems, management, decision-making in conditions of uncertainty.

Download article COMPARATIVE ANALYSIS OF MATHEMATICAL MODELS OF RATING SYSTEMS AND THEIR APPLICATION IN MACRO-TOURNAMENT MANAGEMENT

Rozanova Anna Vyacheslavovna, Student, Department of Big Data Analytics and Video Analysis Methods, Ural Federal University named after the first President of Russia B.N. Yeltsin

Predein Nikita Sergeevich, Student, Department of Big Data Analytics and Video Analysis Methods, Ural Federal University named after the first President of Russia B.N. Yeltsin

Ivliev Trofim Alekseevich, Student, Department of Big Data Analytics and Video Analysis Methods, Ural Federal University named after the first President of Russia B.N. Yeltsin

Tolstov Avdey Tarasovich, Postgraduate Student of the Institute of Radio Electronics and Information Technology – RTF, Ural Federal University named after the first President of Russia B.N. Yeltsin

Saif Mujahed Abdullah Hayel, Senior Lecturer, Department of Big Data Analytics and Video Analysis Methods, Ural Federal University named after the first President of Russia B.N. Yeltsin

Abstract

The purpose of the study is to implement a tool for automatic detection and further normalization of duplicate regulatory and reference information in accounting systems. To solve the problem, a two-level architecture is used, which includes the “HNSW” algorithm for searching for k nearest neighbors, which performs the initial selection of candidates, and the open large language model “Qwen3″ for semantic analysis and development of recommendations for normalization. The developed concept will significantly increase the quality of data stored in ERP/CRM systems, while automating up to 80% of manual operations and reducing operating costs. The research conducted makes a significant contribution to the field of intelligent data processing, offering a targeted solution to one of the most pressing problems of digital transformation.

KEYWORDS: digital transformation of production processes, intelligent approach to automation, normative reference information, normalization of information, semantic analysis, machine learning, large language models, HNSW.

Download article A TOOL FOR NORMALIZING REGULATORY INFORMATION DATA USING A LARGE LANGUAGE MODEL

Petrosyan Anna Surenovna, Student, Department of Big Data Analytics and Video Analysis Methods, Ural Federal University named after the first President of Russia B.N. Yeltsin

Gorbatenkov Timofey Vladimirovich, Student, Department of Big Data Analytics and Video Analysis Methods, Ural Federal University named after the first President of Russia B.N. Yeltsin

Kolchanov Konstantin Viktorovich, Student, Department of Big Data Analytics and Video Analysis Methods, Ural Federal University named after the first President of Russia B.N. Yeltsin

Myagchenko Ilya Dmitrievich, Student, Department of Big Data Analytics and Video Analysis Methods, Ural Federal University named after the first President of Russia B.N. Yeltsin

Saif Mujahed Abdullah Hayel, Senior Lecturer, Department of Big Data Analytics and Video Analysis Methods, Ural Federal University named after the first President of Russia B.N. Yeltsin

Abstract

This article examines a prototype of an intelligent agent (AI agent) integrated into a teaching and learning package (TMP) for the course “Practical Entrepreneurship.” The primary goal of the study is to improve the effectiveness of students’ independent learning activities and provide flexible support for the educational process. An AI agent architecture has been developed to provide adaptive learning support and simplify explanations to students while simultaneously improving the usability of instructors.

KEYWORDS: AI agent, teaching and learning package, adaptive learning, personalized learning, transcription.

Download article AI-AGENT PROTOTYPE FOR THE EDUCATIONAL COURSE AS PART OF THE EDUCATIONAL AND MULTIPLE-COURSE

Contents

Pogulyaeva Lidiya Mikhailovna, Sinichkina Aleksandra Sergeevna, Vorobyov Vasily Vladimirovich, Vorobyov Egor Sergeevich, Kokaya Irakli Varlamovich, Shapovalov Nikita Andreevich, Golovin Andrey Arkadyevich
ANALYSIS AND DEVELOPMENT OF ENVIRONMENTAL SUPPORT MEASURES FOR THE DISTRICTS OF THE SOUTH-EASTERN ADMINISTRATION OF MOSCOW

Liu Yusong, Balungu Daniel Musafiri
OPTIMIZING SMART MANUFACTURING WORKSHOP LAYOUTS USING A DIGITAL TWIN-ENHANCED SLP-GA MULTI-OBJECTIVE MODEL: A PCB CASE STUDY

Ponomareva Olga Alexeevna, Gribov Mikhail Andreevich, Chistyakov Maxim Vladimirovich, Barybin Dmitry Alexandrovich
INFORMATION FLOWS OF A CONSUMER GOODS WAREHOUSE ENTERPRISE AS AN OBJECT OF INFORMATION PROTECTION

Grechitaeva Marina Vyacheslavovna, Shamaeva Ekaterina Fyodorovna
CONTRIBUTION OF CLIMATE-ACTIVE EMISSIONS FROM MAN-MADE RAW MATERIAL LANDFILLS TO ENVIRONMENTAL EMISSIONS

Eburu Evans Chukwuebuka, Balungu Daniel Musafiri
DISCRETE EVENT SIMULATION OF MUNICIPAL MEAT PRODUCTION SYSTEM: BOTTLENECK ANALYSIS AND OPTIMIZATION

Ponomareva Olga Alexeevna, Chistyakov Maxim Vladimirovich, Gribov Mikhail Andreevich, Barybin Dmitry Alexandrovich
OVERVIEW OF NETWORK TRAFFIC CLASSIFICATION METHODS

Pogulyaeva Lidiya Mikhailovna, student, State University of Management

Sinichkina Aleksandra Sergeevna, student, State University of Management

Vorobyov Vasily Vladimirovich, student, State University of Management

Vorobyov Egor Sergeevich, student, State University of Management

Kokaya Irakli Varlamovich, student, State University of Management

Shapovalov Nikita Andreevich, student, State University of Management

Golovin Andrey Arkadyevich, supervisor, associate professor, Department of Environmental Management, State University of Management, PhD in Economics

Abstract

Currently, everyone is familiar with the concept of “ecology”. Anyway, anyone could have heard this term while doing ordinary things. The fact is that now problems with the environmental situation in the world are gaining momentum and through scientific articles, news on television, headlines in the media, conservationists are trying to attract the attention of others. Let’s consider the environmental problems in the districts: Vykhino-Zhulebino, Nekrasovka, Kotelniki, Kapotnya (Southern Administrative District). proposals for solving the spread of environmental problems, we will identify environmental problems in Moscow.

KEYWORDS: ecology, Southern Administrative District, development of support measures, system analysis, environmental problems, education, environmental management.

Download article ANALYSIS AND DEVELOPMENT OF ENVIRONMENTAL SUPPORT MEASURES FOR THE DISTRICTS OF THE SOUTH-EASTERN ADMINISTRATION OF MOSCOW

Liu Yusong, student at the Institute of Radio Electronics and Information Technology – RTF, Ural Federal University named after the first President of Russia B.N. Yeltsin

Balungu Daniel Musafiri, PhD student, Assistant at the Basic Department of Big Data Analytics and Video Analysis Methods, Institute of Radio Electronics and Information Technology, Ural Federal University named after the first President of Russia B.N. Yeltsin

Abstract

In the era of smart manufacturing, optimizing workshop layouts plays a pivotal role in enhancing production efficiency and reducing logistics costs. This study proposes a digital twin-enhanced multi-objective layout optimization model that integrates Systematic Layout Planning (SLP) with a Genetic Algorithm (GA). The model aims to balance logistics efficiency and non-logistics relationships through a comprehensive optimization process. Using a PCB manufacturing workshop as a case study, the proposed SLP-GA model was verified via digital twin simulation built in Anylogic, which enabled dynamic evaluation of production flow and personnel utilization. Results demonstrate that the optimized layout reduces logistics distance by 28.8%, decreases transport personnel by 11, and increases annual output by over 120% compared with the original configuration. The proposed approach effectively supports lean transformation and provides a replicable framework for intelligent, data-driven workshop design in the context of Industry 4.0.

KEYWORDS: smart manufacturing, layout optimization, SLP-GA, digital twin.

Download article OPTIMIZING SMART MANUFACTURING WORKSHOP LAYOUTS USING A DIGITAL TWIN-ENHANCED SLP-GA MULTI-OBJECTIVE MODEL: A PCB CASE STUDY

Ponomareva Olga Alexeevna, Associate Professor, Candidate of Sciences (Engineering), Educational and Research Center “Information Security”, Ural Federal University named after the first President of Russia B.N. Yeltsin

Gribov Mikhail Andreevich, Master’s Student, Educational and Research Center “Information Security”, Ural Federal University named after the first President of Russia B.N. Yeltsin

Chistyakov Maxim Vladimirovich, Master’s Student, Educational and Research Center “Information Security”, Ural Federal University named after the first President of Russia B.N. Yeltsin

Barybin Dmitry Alexandrovich, Master’s Student, Educational and Research Center “Information Security”, Ural Federal University named after the first President of Russia B.N. Yeltsin

Abstract

The aim of the study is to assess information security threats at warehouse storage enterprises and develop measures to address them. The work involved an analysis of regulatory documents, publications in specialized journals, and expert opinions in the fields of logistics and information security, as well as an examination of incidents related to data leaks in logistics companies. The study also explored the specifics of information flow in warehouses and vulnerabilities arising from the implementation of new technological solutions. The article proposes a model of the information exchange and processing system in a warehouse, based on the IDEF0 functional modeling standard. Key information security threats, such as malware, phishing, DDoS attacks, web application vulnerabilities, and social engineering, were identified. For each threat, countermeasures were proposed, including the use of modern encryption protocols, two-factor authentication, regular software updates, and employee training. Recommendations were provided to minimize risks associated with the use of RFID tags, barcodes, and QR codes, as well as to ensure the security of wireless data transmission channels. The study emphasizes the need for a comprehensive approach to ensuring information security in warehouses, encompassing technical, organizational, and educational measures. The results of the research can be used to develop methodological recommendations for data protection at warehouse storage enterprises.

KEYWORDS: information protection, warehouse storage enterprises, vulnerabilities, warehouse management systems, social engineering.

Download article INFORMATION FLOWS OF A CONSUMER GOODS WAREHOUSE ENTERPRISE AS AN OBJECT OF INFORMATION PROTECTION

Grechitaeva Marina Vyacheslavovna, Doctor of Biological Sciences, Professor, Professor of the Department of Environmental Management, State University of Management

Shamaeva Ekaterina Fyodorovna, Candidate of Technical Sciences, Assoc. Prof., Associate Professor of the Department of Environmental Management, State University of Management

Abstract

The study focuses on analyzing the emission of climate-active substances and developing a system for regulating greenhouse gas emissions in the Russian Federation in the context of global climate change. The study provides a comprehensive assessment of the structure of emissions of major greenhouse gases, with a detailed breakdown by sectoral sources. It also analyzes the dynamics of greenhouse gas emissions in Russia over the period 1990-2023, revealing a decrease of 33.8% without taking into account forestry and a decrease of 62.5% with its inclusion. The potential of the waste sector as a significant source of methane emissions (up to 65 million tons of CO₂-eq/year) has been studied. A comprehensive approach has been applied, including the use of IAEK-based calculation methods for monitoring, the analysis of data from the Rosgidromet observation network, and the evaluation of technological solutions for capturing and utilizing landfill gas. The economic efficiency of implementing methane utilization technologies at municipal solid waste landfills has been substantiated.

KEYWORDS: greenhouse gases, carbon footprint, climate policy, methane, waste sector, carbon landfills, carbon neutrality, Paris Agreement.

Download article CONTRIBUTION OF CLIMATE-ACTIVE EMISSIONS FROM MAN-MADE RAW MATERIAL LANDFILLS TO ENVIRONMENTAL EMISSIONS