Scientific journal
Economics. Law. Innovation

Abstract

The purpose of this article is to study the advantages and limitations of the digital transformation of seaports, taking into account the difficulties of interaction with all stakeholders, to identify the sources of the main barriers hindering the maximum effect of digital transformation. The integration of advanced technologies is analyzed in the context of digitalization of cargo handling processes, increasing operational efficiency and ensuring smooth interaction between participants.
The study identifies key benefits of digitalization of ports. However, it also highlights barriers to digital adoption. The study findings suggest that successful implementation of digital solutions in seaports requires harmonized efforts from governments, private sector stakeholders and technology providers to ensure a holistic and efficient digital ecosystem. The study concludes that digital transformation is essential to improve the performance of seaports, ensure long-term sustainability and adapt to the changing landscape of international trade.

Keywords: cost-effectiveness of digitalization, digitalization, digital transformation, seaport, smart port

References:

Novikova T. O. The Role of Maritime Transport in International Trade. Yevraziyskiy nauchnyy zhurnal. 2016. No. 8. Available at: https://journalpro.ru/upload/uf/4f7/4f73e9bbc4e8ae35c0e2597eb8c29c7b.pdf  (In Russ.).
2.  Kupriyanovskaya Y. V., Kupriyanovsky V. P., Klimov A. A., Namiot D. E., Dolbnev A. V., Sinyagov S. A., Lipuntsov Y. P., Arsenyan A. G., Evtushenko S. N., Larin O. N. Smart Container, Smart Port, BIM, Internet of Things and Blockchain in the Digital System of World Trade. Mezhdunarodnyy zhurnal otkrytykh informatsionnykh tekhnologiy. Available at: https://cyberleninka.ru/article/n/umnyy-konteyner-umnyy-port-bim-internet-veschey-i-blokcheyn-v-tsifrovoy-sisteme-mirovoy-torgovli (In Russ.).
3.  Panamareva O. N. Features of Digital Transformation of the Transport Industry and its Impact on the Development of Russian Ports. Vestnik MFSA. 2022. No. 2. pp. 76–99. Available at: https://cyberleninka.ru/article/n/osobennosti-tsifrovoy-transformatsii-transportnoy-otrasli-i-ee-vliyanie-na-razvitie-portov-rossii (In Russ.).
4.  Masyuk N. N., Bludik A. R. Modern Trends of Digital Transformation in the Maritime Industryю EGI. 2022. No. 44 (6). pp. 203–207. Available at: https://cyberleninka.ru/article/n/sovremennye-tendentsii-tsifrovoy-transformatsii-v-morskoy-otrasli (In Russ.).
5.  Klimov A. A., Kupriyanovsky V. P., Alenkov V. V., Anisimov K. O., Volodin A. B., Kupriyanovskaya Yu. B., Kupriyanovskaya Yu. V. Smart Technologies in Ports and Shipping as Connected Digital Twins of Shore and Ship in a Multimodal Environment. Mezhdunarodnyy zhurnal otkrytykh informatsionnykh tekhnologiy. 2020. No. 3. pp. 75–91. Available at: https://cyberleninka.ru/article/n/umnye-tehnologii-v-portah-i-v-sudohodstve-kak-svyazannye-tsifrovye-dvoyniki-berega-i-sudna-v-multimodalnom-okruzhenii (In Russ.).
6.  Karapetyants I. V., Tolstykh T. O., Shkarupeta E. V. Transformation of Logistics Processes in the Digital Economy. REGION: sistemy, ekonomika, upravleniye. 2017. No. 3 (38). pp. 104–110. Available at: https://cyberleninka.ru/article/n/transformatsiya-logisticheskih-protsessov-v-tsifrovoy-ekonomike (In Russ.).
7.  Zub I. V. et al. Information Systems as a Tool to Increase the Productivity of Seaports. Vestnik Gosudarstvennogo universiteta morskogo i rechnogo flota imeni admirala S. O. Makarova. 2022. Vol. 14. No. 2. pp. 218–229. DOI: 10.21821/2309-5180-2022-14-2-218-229. (In Russ.).
8.  Gornova A. M., Dolgikh A. D. Shipping in the Aspect of Cybersecurity. Arkticheskoye obozreniye. 2019. No. 5. pp. 52–58. (In Russ.).
9.  Dementiev D. S., Ashov I. V. Marine Cybersecurity – Situation and Problems. Comprehensive research in the fishing industry. Materials of the VI International Scientific and Technical Conference of Students, Postgraduate Students and Young Scientists. Vladivostok, 2021. Dal'nevostochnyy gosudarstvennyy tekhnicheskiy rybokhozyaystvennyy universitet, 2021. pp. 300–304. (In Russ.).
10. Kamennaya E.V., et al. Prospects for the Use of Blockchain Technology to Ensure Information Security in Maritime Transport. Transportnoye delo Rossii. 2018. No. 6. pp. 194–197. (In Russ.).
11. Tsyguta A.N., Ampleyeva A.V. Cybersecurity as One of the Factors of Marine Industry Development. Transport: problems, goals, prospects (Transport 2020). Materials of the All-Russian Scientific and Technical Conference. Perm, 2020. Permskiy filial FGBOU VO «Volzhskiy gosudar-stvennyy universitet vodnogo transporta». 2020. pp. 65–68. (In Russ.).
12. Deriglazov A. P., Pavlova E. A. Research of the Factors Influencing the Choice of Tools for Digital Transformation of Marketing Companies. Vestnik Academii Znaniy. 2024. No. 2. pp. 148–152. (In Russ.).
13. Elmasry T. et al. Digital Middle East: Transforming the Region into a Leading Digital Economy. McKinsey& Company. Official website. Available at: http://www.mckinsey.com/global-themes/middle-east-and-africa/digital-middle-east-transforming-the- region-into-a-leading-digital-economy
14. Tsyganov V. V. On the Strategic Management of Transport and Logistics Infrastructure of the Russian Federation. Avtomatizatsiya v promyshlennosti. 2024. No. 5. pp. 41–44. DOI 10.25728/avtprom.2024.05.08. (In Russ.).
15. Architecture of the National Digital Platform for Freight Transportation Presented at the «DIGITAL TRANSPORTATION». Eksperty bezopasnosti. Informatsionnoye agentstvo. Available at: https://securityexp.ru/tpost/uav9mzyhz1-arhitektura-natsionalnoi-tsifrovoi-platf#subscribe (In Russ.).

Abstract

The article considers the concept of organizational projectivity as a central element of assessing the maturity of an innovative company in the field of project management. Organizational projectivity is defined as the ability of an innovative company to anticipate changes, adapt to them, and effectively implement projects under conditions of uncertainty. The key aspects of projectivity are analyzed in detail: strategic vision, flexibility and adaptability, project management culture, use of technology and data, and knowledge management. Methods are proposed that allow increasing the level of maturity of an innovative company for each of the criteria considered. An illustrative example is given.
The scientific novelty of the study lies in the integrated approach to assessing the maturity of an innovative company in the field of project management through the prism of organizational projectivity. Unlike traditional assessment models that focus on processes and methodologies, this work focuses on the ability of an innovative company to forecast, adapt and innovate. A structured model of organizational projectivity is substantiated, reflecting five key elements: strategic vision, flexibility, project management culture, use of technology and knowledge management. This approach allows not only to assess the current level of maturity of an innovative company, but also to determine the directions for its further development. In addition, the work contributes to the theory of project management, expanding the understanding of the role of organizational culture and strategic thinking in the successful implementation of projects.
The practical significance of the study lies in providing innovative companies with specific tools and recommendations to improve their maturity in the field of project management. Practical steps are proposed to develop organizational projectivity, such as the introduction of flexible methodologies, the creation of a culture of change, the use of modern technologies and knowledge management systems. In addition, the work can be useful for project managers and executives who seek to integrate project management into strategic planning and make it an integral part of organizational culture.

Keywords: adaptation to changes, company maturity, organizational projectivity, project management, strategic management

References:

1. Trofimova E.V., Trofimov V. V. Theoretical Foundations for Constructing a System for Assessing the Maturity Level of Project Management for the Innovative Development of Socio-Economic Systems. Innovacii i Investicii. 2025. No. 2. (In Russ.).
2. Taras'ev A. A. et al. Development of a Methodological Approach to Assessing a Company’s Maturity in the Field of Project Management. Moskovskij ekonomicheskij zhurnal. 2022. Vol. 7. No. 11. (In Russ.).
3. Shishova M. A., Chernova A.V. Organizational Projectivity as a Factor in Successful Project Management. Upravlenie i informacionnye tekhnologii. 2019. No. 4 (15). pp.123–130. (In Russ.).
4. Krechetov S. D Review of Modern Maturity Models of Organizational Project Management. In the collection: Development of Modern Science and Technology in the Context of Transformation Processes. Collection of Materials of the XI International Scientific and Practical Conference. Sankt-Peterburg. 2023. pp. 77–83. (In Russ.).
5. Najdis I. O. Analysis of Project Management Maturity Models. Modern Science. 2020. No. 6-1. pp. 144–151. (In Russ.).
6. PMBOK® Guide. Project Management Institute (PMI). 2021. 250 P.
7. Capability Maturity Model Integration (CMMI) for Development Version 3.0. [Электронный ресурс]. – Режим доступа: https://kpmg.com/in/en/services/advisory/consulting/business-consulting/ transformation-business-excellence/capability-maturity-model-integration-cmmi.html
8. Kryukov V. V., Razumova Y. V. Model for Assessing the Maturity of the Project Management System of Regional Enterprises. Azimut nauchnyh issledovanij: ekonomika i upravlenie. 2022. Vol. 11. No. 2 (39). pp. 40–44. (In Russ.).
9.  The Relationship of BPCS Documents with Processes and Knowledge Areas in Accordance with the PMBOK.Available at: http://pmdoc.ru (In Russ.).

Abstract

The article analyzes the modern innovation policy of Russian universities. The review of foreign approaches to planning and implementation of innovation policy of higher education institutions, as well as the Russian experience allowed to identify the key features of innovation policy. The role of state policy in the formation and realization of innovation policy of universities is revealed. The analysis of the main directions of innovation policy of Russian universities participating in the state program «Priority 2030» showed that one of the key ways to improve innovation policy at a university is to develop innovation ecosystems based on interaction between the university and external stakeholders. It is suggested to categorize the main aspects of university innovation policy, which encompasses enhancing seven critical areas: education, research and development, entrepreneurship, building relationships with employers and other stakeholders, infrastructure development, digitalization and digital transformation, as well as organizational and managerial processes. Drawing on an analysis of literature and sources, as well as a study of the experience of Russian universities in implementing innovation policy, we propose to systematize the main forms and types of innovation policy. A framework is proposed that outlines the role and position of university innovation policy within the innovation ecosystem. This framework can be used by universities when creating development strategies that incorporate an ecosystem approach.

Keywords: development of human capital, ecosystem, innovation policy, university, innovations, university competitiveness

References:

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2.  Federal Law «On Innovative Scientific and Technological Centers and on Amendments to Certain Legislative Acts of the Russian Federation» dated 07/29/2017 No. 216-FZ (as amended on 08.08.2024). SPS ConsultantPlus. Available at: https://www.consultant.ru/document/ cons_doc_LAW_221172/ (In Russ.).
3.  The National Program «Digital Economy of the Russian Federation» Approved by the Minutes of the Meeting of the Presidium of the Council under the President of the Russian Federation for Strategic Development and National Projects dated June 4, 2019 No. 7. Ministry of Digital Development, Communications and Mass Media of the Russian Federation. Official website. Available at: https://digital.gov.ru/ru/activity/directions/858/ (In Russ.).
4.  National project «Science and Universities». Ministry of Education and Science of Russia. The official website. Available at: https://национальныепроекты.рф/projects/nauka-i-universitety/ (In Russ.).
5.  Resolution of the Government of the Russian Federation dated 05/13/2021 No. 729 «On Measures to Implement the Strategic Academic Leadership Program "Priority 2030"». SPS ConsultantPlus. Available at: https://www.consultant.ru/document/cons_ doc_LAW_38 4628/ (In Russ.).
6.  Rating «Index of Inventive Activity of Russian Universities» – 2023. Research division of the media holding Expert. Official website. Available at: https://expert-ural.com/analytics/ratings/reyting-indeks-izobretatelskoy-aktivnosti-rossiyskih-universitetov/ (In Russ.).
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8.  Sitnikova S. E. Innovative Policy of Development of Local Economic Systems in Russia and Foreign Countries. Ekonomika  I  predprinimatel’stvo.  2024.  No.  4 (165). pp. 204–207. (In Russ.).
9.  Mityakov S. N., Mityakov E. S., Gorina T. V. System of Indicators for Evaluating Innovation Policy of Higher Technical Educational Institutions. Ekonomika, predprinimatel’stvo i pravo. 2024. Vol.  14. No. 4. pp. 1207–1228. (In Russ.).
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12.   Aranha E. A., Carvalho S. Innovation Policy, Open Innovation and Business Model in the University. Journal of Technology Management & Innovation. 2022. Vol. 17. No. 1. pp. 110–121.
13.   Results of the Student Startup competition. Foundation for the Promotion of Innovations. Official website. Available at: https://fasie.ru/competitions/? PROGRAM_ ID=945 (In Russ.).
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Abstract

In today's realities, the aviation industry is facing the challenges of growing passenger traffic (by 5.8% annually until 2040), as well as increased passenger expectations in terms of quality and speed of service. The article is devoted to the research of the innovative concept of airport development – "Airport 4.0". This is a new approach to airport management aimed at improving their efficiency, safety and convenience for passengers through the use of modern digital technologies. The purpose of the study is to assess the potential and readiness of Russian airports to implement the Airport 4.0 concept. The article examines examples of successful application of the Airport 4.0 concept at world airports, as well as an assessment of the degree of digital maturity of Russia's largest airports, such as Sheremetyevo and Pulkovo. The implementation of the Airport 4.0 concept is an urgent and promising direction for the development of the aviation industry. The study showed that this concept will not only increase the competitiveness of domestic airports, but also allow the industry to successfully adapt to modern challenges. This underscores the need to continue scientific research in this area, since the concept of Airport 4.0 is in its infancy and requires further theoretical and practical understanding.

Keywords: aviation industry, digitalization, digital transformation, digital technologies, smart airport, the concept of «Airport 4.0»

References:

1. Evolution Of Airports – Travel Trends In The Next 30 Years. Oliver Wyman Forum. Available at: https://www.oliverwymanforum.com/mobility/2023 /jun/airport-evolution-travel-trends-by-2050.html
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7. Golovchenko G. V., Chinyuchin Yu. M, Daletskii S. V. Analysis of Priorities and Trends in Automation of Russian airports. Nauchnyi vestnik GoSNII GA. 2021. No. 37. pp. 84–94. (In Russ.).
8. Zubareva S., Bukrinskaya E. M. Digital Transformation of Airports. Smart Airport. Ekonomika i biznes: teoriya i praktika. 2020. No. 11-2. pp. 10–14. (In Russ.).
9.  Ermakov A. A. Digital Transformation in the Aviation Industry. Moskovskiy ekonomicheskiy zhurnal. 2023. No.1. pp. 294–304. (In Russ.).
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12. Airport Digital Transformation from Operational Performance to Strategic Opportunity. Amadeus. Available at:https://doingbusiness.ro/article/amadeus-airport-40-study-digital-airport-transformation-4167
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14. Global Passenger Traffic Expected to Recover by 2024 and Reach 9.4 Billion Passengers. ACI World. Available at: https://aci.aero/2023/09/27/global-passenger-traffic-expected-to-recover-by-2024-and-reach-9-4-billion-passengers/
15. Smart Airport Market Report. IMARC Group. Available at: https://www.imarcgroup.com/smart-airport-market
 16. Atlanta to Expand Biometric Technology to Entire International Terminal. Cities Today. Available at: https://cities-today.com/atlanta-to-expand-biometric-technology-to-entire-international-terminal/?utm_source=chatgpt.com
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19. Passenger Traffic at Russian Airports 2022. Avia Adv. Available at:https://www.avia-adv.ru/stat/airports-ru-2022.htm (In Russ.).
20. Russian Airports. Passenger Traffic Statistics for 2023. Avia Adv. Available at: https://www.avia-adv.ru/news/2024/03-13.htm. (In Russ.).
21. Global Air Travel Demand Continued Its Bounce Back in 2023. IATA. Available at: https://www.iata.org/en/pressroom/2024-releases/ 2024-01-31-02/
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DOI: 10.48550/arXiv.2112.14333.

Abstract

Over the past decade, the issue of innovation development has become significant for the higher education market. Universities are striving to implement various forms of organizational innovations in order to establish a sustainable position on both national and international markets. Researchers emphasize the importance of analyzing global experience with tools for developing innovation potential and the need to study their impact on the ability to respond to changes in the external environment. An important element of sustainable innovation development is the use of industry-standard tools for enhancing innovation capacity to ensure effective implementation of educational programs and strengthen the positions of universities in the market. The aim of this research is to identify thematic groups of publications dedicated to international rankings as an instrument for developing the innovation potential of universities. As materials, scientific articles from 1999 to 2020 were used from the Web of Science database. The method of investigation involves conducting scientometric analysis, presenting topics of scientific research related to studying international rankings as one of the key instruments of innovation potential in universities. Based on the results, thematic clusters have been identified and key articles within each cluster have been examined. It was concluded that international rankings play an important role as one of the tools for developing innovation potential for universities, although their significant limitations are highlighted. Directions for further research have been formulated, focusing on the necessity of comprehensive evaluation of innovation potential and empirical testing of the efficiency of other tools for developing innovation potential in universities.

Keywords: innovative development of universities, innovative potential of universities, international university rankings, tools of innovative development

References:

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3.   Zuntova I. S., Veselovsky M. Ya. Innovative Assessment and Analysis of the Effectiveness Level of University Activities. Vestnik Gosudarstvennogo universiteta prosvescheniya. Series: Economics. 2024.No. 4. pp. 58–69. (In Russ.).
4.   Khamirzova S. K., Khabechirova Z. S. Development of Innovative Activity of Russian Universities in Modern Conditions. Vestnik Adygeyskogo gosudarstvennogo universiteta. Series 5: Economy. 2024. No. 4 (342). pp. 97-107. (In Russ.).
5.   Iacuzzi S., Fedele P., Garlatti A. Using Accreditation to Leverage Innovation in Healthcare: Evidence from an Oncological Centre. International Journal of Public Administration. 2020.  DOI:10.1080/01900692.2020.1795671
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8.   Adushkin I. I. Essense of Innovation Potential. Reshetnevskie chteniia. 2013. Vol. 2. No. 17. pp. 434–436. (In Russ.).
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Abstract

The objective of this work is to develop and experimentally implement the concept of an information system (IS) for precision farming, providing for the collection, processing, analysis and visualization of data on the state of the soil, plants and the environment. Such an IS facilitates informed decision-making on crop management, optimization of resource use and creation of a basis for integration with enterprise resource planning (ERP) systems, which ultimately leads to increased efficiency and sustainability of agricultural production. The information and analytical basis of the presented study covers a set of international standards regulating the processes of development and implementation of ICT projects, specialized publications and monographs devoted to the design of management IS and precision farming, as well as empirical material obtained on the basis of the practical experience of organizations.
Methodological approaches include a substantive analysis of literary sources, a comparative analysis of known IS formation methods, a study of the internal structure of technological processes and tools of agricultural enterprises, as well as modern statistical methods.
The result was the creation of an IS concept designed for agricultural enterprises with an emphasis on precision farming. This system is designed for complex processing, detailed study and graphic presentation of information on the state of the soil cover, growth of agricultural crops and environmental factors.
Testing of the pilot version of the IS at an agricultural enterprise showed that its use helps to increase crop yields and reduce fertilizer costs. Such an IS can be used as a basis for creating a digital agricultural enterprise, and the research results can be used to develop regulatory and reference documentation for the implementation of digital technologies for precision farming.

Keywords: cost optimization, crop yield, digital agriculture, digital transformation, GIS, information system, machine learning, precision farming, sensors

References:

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2. Thiruvengadam V., Yang Y., Chen B. A Review of Precision Agriculture Technologies for Site-Specific Nutrient Management. Agronomy. 2022. No. 12 (8). P. 1767.
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Abstract

This paper proposes an approach to modeling innovation processes in high-tech enterprises using economic and mathematical models. The relevance of the study stems from the need for a precise evaluation of innovation investment effectiveness under rapid technological changes and external uncertainty. The suggested model accounts for time lags between investments and results, as well as memory effects that describe declining performance in the absence of continuous innovation. The model describes profitability dynamics as a function of investment volumes in both innovation and operational activities, as well as the internal state of the enterprise. The concept of competitiveness is formalized, and quantitative indicators are introduced to reflect the organization’s sensitivity to innovation-related impacts. The paper also considers the problem of optimal resource allocation across innovation projects under financial and technological constraints.
The novelty of the research lies in applying finite automata to enterprise state analysis and developing a predictive model integrated into digital innovation management systems. The proposed approach enables informed managerial decision-making concerning the prioritization and funding of innovative activities, thereby enhancing the competitiveness and sustainability of high-tech enterprises.

Keywords: competitiveness, economic and mathematical modeling, innovation processes, investment, high-tech enterprise

References:

1. Dodgson M., Gann D., Phillips N. The Oxford Handbook of Innovation Management. Oxford: Oxford University Press, 2023. 680 p.
2. Dosi G., Marengo L., Pasquali C. How knowledge shapes the evolution of innovation systems. Research Policy. 2021. Vol. 50. No. 9. P. 104330.
3. Fichman R. G., Dos Santos B. L., Zheng Z. Digital Innovation as a Fundamental and Powerful Concept in the Information Systems Curriculum. MIS Quarterly. 2022. Vol. 46. No. 1. pp. 223–251.
4. Kline S. J., Rosenberg N. An Overview of Innovation. The Positive Sum Strategy: Harnessing Technology for Economic Growth. Washington: National Academies Press, 2021. P. 275–305.
5. Shamin R. V. Using Finite Automata in the Analysis of Economic Systems. Zhurnal prikladnoy informatiki. 2021. No. 4. pp. 22–30. (In Russ.).
6. Gulyaev V. V. Mathematical Modeling in Economics. M.: Yurait, 2021. 312 p. (In Russ.).
7. Chursin A. A. Competitiveness of Knowledge-Intensive Enterprises: Theory and Practice. M.: Infra-M, 2020. 302 p. (In Russ.).

Abstract

The article examines the application of Large Language Models (LLMs) in legal case analysis. Their capabilities and limitations in processing legal documents are analyzed, along with the impact of non-monotonic and deontic logic on inference accuracy. GPT-4o, GigaChat, and YandexGPT were tested to assess their precision in handling legal information. Key issues were identified, including hallucinations, incorrect legal references, and difficulties in interpreting logical paradoxes. To enhance performance, several adaptation measures were proposed, including the use of a system prompt, a RAG repository of legal documents, and optimization of text generation parameters. The results demonstrate that the improved model provides more accurate legal insights and significantly speeds up the process of legal analysis.

Keywords: deontic logic, GPT, Large Language Models, Legal Case Analysis, Llama non-monotonic logic

References:

1. Wang X., Zhang X., Hoo V., Shao Z., Zhang X. LegalReasoner: A Multi-Stage Framework for Legal Judgment Prediction via Large Language Models and Knowledge Integration. IEEE Access. 2024. Vol. 12. DOI: 10.1109/ACCESS.2024.3496666.
2. Gutiérrez J. D. Critical Appraisal of Large Language Models in Judicial Decision-Making. Elgaronline. 2024.
 DOI: 10.4337/9781803922171.00033.
3. Nelson J. The Other ‘LLM’: Large Language Models and the Future of Legal Education. European Journal of Legal Education. 2024. Vol. 5. No. 1. pp. 127–155.
4. Antonelli G. A., Strasser C. Non-Monotonic Logic. Stanford Encyclopedia of Philosophy. Available at: https://plato.stanford.edu/entries/logic-nonmonotonic/
5. Wright G. H. von. Norm and Action: A Logical Inquiry. Routledge & Kegan Paul1963. 214 P. (In Eng.).
6. Guha N., Nyarko J., Ho D., Ré C., Chilton A., Chohlas-Wood A., Li Z. LegalBench: A Collaboratively Built Benchmark for Measuring Legal Reasoning in Large Language Models. Available at: https://arxiv.org/ pdf/2308.11462
7. Martin L., Whitehouse N., Yiu S., Catterson L., Perera R. Better Call GPT: Comparing Large Language Models Against Lawyers. Available at: https://arxiv.org/ pdf/2401.16212
 8. Cheong I., Xia K., Feng K. K., Chen Q. Z., Zhang A. X. I Am Not a Lawyer, But…: Engaging Legal Experts Towards Responsible LLM Policies for Legal Advice. Available at: https://arxiv.org/ pdf/2402.01864
9. Izzidien A., Sargeant H., SteffekF. LLM vs. Lawyers: Identifying a Subset of Summary Judgments in a Large UK Case Law Dataset. Available at: https://arxiv.org/pdf/ 2403.04791
10. Sun J., Dai C., Luo Z., Chang Y., Li Y. LawLuo: A Chinese Law Firm Co-Run by LLM Agents. Available at: https://arxiv.org/pdf/2407.16252v1
11. Parmar M., et all. Towards Systematic Evaluation of Logical Reasoning Ability of Large Language Models. Available at: https://arxiv.org/ html/2404.15522v1
 12. Von Wright G. H. Deontic Logic. Mind. Vol. 60. No. 237. pp. 1–15.
13. Russian-Language Neural Network from Sber. GigaChat. Available at: https://giga.chat/ (In Russ.).
14. BYAM from Yandex. YandexGPT 3. Available at: https://ya.ru/ai/gpt-3 (In Russ.).
15. GPT-4o. OpenAI. Available at: https://openai.com/index/gpt-4/
16. Recommendations for Creating System Prompts for LLMs. Available at: https://x.com/gdb/status/1878489681702310392?mx=2
 17. Non-Monotonic Logic. Stanford Encyclopedia of Philosophy. Available at: https://plato.stanford.edu/entries/logic-nonmonotonic/
18. Retrieval-Augmented Generation (RAG) Method. Hugging Face. Available at: https://huggingface.co/docs/transformers/perplexity
19. Li H., Dong Q., Chen J., Su H., Zhou Y., Ai Q., Liu Y. LLMs-as-Judges: A Comprehensive Survey on LLM-Based Evaluation Methods. Available at: https://arxiv.org/pdf/2412.05579
20. Iurev R. N., Bessmertny I. A., Gruzdeva A. S. Quality Metrics of Legal Texts. Ekonomika. Pravo. Innovacii. 2024. No. 2. pp. 81–93. (In Russ.).

Abstract

The study is devoted to improving the quality of management of organizational systems in residential complexes based on the analysis of retrospective data generated by complaints from residents. An information system is proposed, consisting of a mobile application, a web platform and an analytical module, which organizes orderly communication between clients, the management of an apartment building and the analytical part. The mobile application allows residents to generate applications, which are then processed on the web platform. The web platform acts as an intermediary that ensures the receipt, analysis and coordination of requests. The analytics module stores application data and analyzes it using a time-series forecasting model for residents about breakdowns. The presented model is based on the SARIMA method. In the process of data preprocessing, the types of breakdowns were manually classified, duplicates were removed, and data on fault complaints was selected. To select hyperparameters, a grid search algorithm that provides the lowest AIC value was used. The model was tested on a test sample and new data previously unknown to it. The limitations of the study are discussed and directions for future research are suggested, such as improving data quality and using hybrid models.

Keywords: apartment building, complaints from residents, incident forecasting, information system, organizational systems management, proactive management, SARIMA, time series model

References:

1. TOP 15 Best Housing and Communal Services Programs for Management Companies and Homeowners Associations for Managing Apartment Buildings. Available at: https://vc.ru/links/1468566-top-15-luchshih-zhkh-programm-dlya-uk-i-tszh-dlya-upravlenie-mnogokvartirnymi-domami (In Russ.).
2. Gavrilenko I. G., Khisaeva A. I. On the Problems Hindering the Effective Management of Apartment Buildings. Vestnik IrGTU. 2015. No. 6 (101). Available at: https://cyberleninka.ru/article/n/o-problemah-prepyatstvu yuschih-effektivnomu-upravleniyu-mnogokvartirnymi-domami (In Russ.).
3. Zastela M. Yu., Skachkov V. A. System of Accounting, Control and Execution of Decisions on Complaints of Citizens in the Sphere of Housing and Communal Services. Available at: https://cyberleninka.ru/article/n/sistema-ucheta-kontrolya-i-ispolneniya-resheniy-po-zhalobam-grazhdan-v-sfere-zhilischno-kommunalnogo-hozyaystva (In Russ.).
4. Dyachenko O. N. Digitalization in the Housing and Communal Services of the Bryansk Region: the «SMART HOUSE» System. Upravleniye i tsifrovizatsiya: natsional'noye i regional'noye izmereniye. Collection of articles from the national scientific and practical conference with international participation. Bryansk. Bryansk, 2021. pp. 150–153. (In Russ.).
5. Kuzmin K. M., Kyashkin V. E., Evdokimov I. V. Design of an Information System for Complex Automation of Activities of Management Organizations in the Housing and Communal Services Sector. Novaya nauka: problemy i perspektivy. 2016. No. 10-1. pp. 152–155. (In Russ.).
6. Dubrovin M. G. Concept of Proactive Monitoring and Management of IT Infrastructure Objects. ITNOU: Informatsionnyye tekhnologii v nauke, obrazovanii i upravlenii. 2020. No. 1 (15). pp. 44–49. (In Russ.).
8. Plotnikova I. A., Sorokina I. V. Problems of Development of Modern Housing and Communal Services. Problemy razvitiya territorii. 2019. No. 6 (104). pp. 52–68. (In Russ.). DOI: 10.15838/ptd.2019.6.104.4.
8. Gorpolskaya E. I. Evaluation of the Efficiency of Services and Work on the Maintenance and Repair of Common Property in an Apartment Building. Mezhdunarodnyy zhurnal prikladnykh nauk i tekhnologiy «Integral». 2019. No. 1. pp. 134–141. (In Russ.).
9. Novgorodov V. Assessment of Technical Maintenance of Common Property of an Apartment Building. Available at: https://www.law.ru/article/24648-obslujivanie-obshche go-imushchestva-mnogokvartirnogo-doma (In Russ.).
10. Responsibilities of the Management Company: How to Eliminate Accidents and Malfunctions? Available at: https://rskrf.ru/consumer_ rights/reviews/kak-dobitsya-ustraneniya-avarii-v-dome/ (In Russ.).
11. Costa D. G. et al. A Survey of Emergencies Management Systems in Smart Cities. Available at: https://ieeexplore.ieee.org/document/9787492?denied=
12. «Doma» (Doma.ai): An Online Platform for Automating the Work of Management Companies (MC) and Homeowners' Associations. Available at: https://doma.ai/ (In Russ.).
13. 1C: Housing and Utilities Management Company and RCC: an Industry Solution for Automating the Main Business Processes of Organizations Managing Apartment Buildings. Available at: https://umsol.ru/develops/zhkh-rkc/ (In Russ.).
14. Pranshu Aggarwal. SARIMA Using Python – Forecast Seasonal Data. Available at: https://www.wisdomgeek.com/development/machine-learning/sarima-forecast-seasonal-data-using-python/
15. Box G. E. P. et al. Time Series Analysis: Forecasting and Control. John Wiley & Sons. 2015.
16. Satin A. P., Le T. B., Prus Yu. V. Forecasting the Readiness of Fire-Fighting Equipment Based on the Markov Model of Breakdowns and Restoration. Available at: https://agps-2006.narod.ru/ttb/2012-5/17-05-12.ttb.pdf (In Russ.).
17. Novikov G. Using Methods for Searching Patterns in a Sequence of Events to Predict Failures of Complex Technical Systems. Collection of works of the 39th interdisciplinary school-conference of the IITP RAS «Information technologies and systems 2015». Available at: http://itas2015.iitp.ru/pdf/1570207677.pdf (In Russ.).
18. Dubrovin M. G. Concept of Proactive Monitoring and Management of IT Infrastructure Objects. ITNOU: Informatsionnyye tekhnologii v nauke, obrazovanii i upravlenii. 2020. No. 1 (15). pp. 44–49. (In Russ.).

19. Efimova A. A., Gavrilyuk E. S. Proactive Maintenance in the Housing and Communal Services Sector as a Trend of Technological Development. Ekonomika. Pravo. Innovacii. 2024. No. 2. pp. 15–23. (In Russ.).

Abstract

The growing competition among developers of IT projects requires constantly increasing the speed of development. This, in turn, negatively affects the quality and cost of the finished product. At the same time, the decrease in speed and quality and the increase in cost are not related to the low qualifications of an individual developer, but to the poor organization of the development process. The subject of the research is the most popular methodologies and approaches and the methods and tools used in them. The purpose of the study is to systematize knowledge about modern methods and tools, determine their compatibility, evaluate the positive and negative sides in order to understand the feasibility of their combination aimed at reducing limitations. The study conducted a literature review to identify the methods and tools used in the most popular methodologies and approaches. Special attention is paid to their applicability and limitations. A survey was prepared and conducted among industry representatives in order to identify the methods and tools used in practice. Based on the analysis of data obtained during the review of literature sources and the survey, recommendations were made on the selection and use of methods and tools to ensure applicability in more cases and reduce restrictions.

Keywords: Agile, agile methodologies, DevOps, hybrid methodologies, software development methodologies, process automation, SCRUM, software engineering, traditional methodologies

References:

1. Akinsola J.E.T., Ogunbanwo A.S., Okesola O.J., Odun-Ayo I.J., Ayegbusi F.D., Adebiyi A.A. Comparative Analysis of Software Development Life Cycle Models (SDLC). Advances in Intelligent Systems and Computing, Springer, Cham. 2020. Vol. 1224. pp. 310–322. DOI: 10.1007/978-3-030-51965-0_27.
2. Shafiee S., Wautelet Y., Hvam L., Sandrin E., Forza C. Scrum Versus Rational Unified Process in Facing the Main Challenges of Product Configuration Systems Development. Journal of Systems and Software. 2020. Vol. 170. DOI: 10.1016/j.jss.2020. 110732.
3. Ibrahim M., Aftab S., Bakhtawar B., Ahmad M., Iqbal A., Aziz N., Javeid M., Ihnaini B. Exploring the Agile Family: A Survey. International Journal of Computer Science and Network Security. 2020. Vol. 20 (10). pp. 163–179. DOI: 10.22937/IJCSNS. 2020.20.10.22.
4. Saravanos A., Curinga M. Simulating the Software Development Lifecycle: The Waterfall Model. Applied System Innovation. 2023. Vol. 6 (6). P. 108. DOI: 10.3390/asi6060108.
 5. Graessler I., Hentze J. The New V-Model of VDI 2206 and its Validation. Automatisierungstechnik. 2020. Vol. 68 (5). pp. 312–324. DOI: 10.1515/auto-2020-0015.
6. Žužek T., Kušar J., Rihar L., Berlec T. Agile-Concurrent hybrid: A Framework for Concurrent Product Development Using Scrum. Concurrent Engineering: Research and Applications. 2020. Vol. 28 (1). pp. 1–10. DOI: 10.1177/1063293X20958541.
7. Grebic B., Stojanović A. Application of the Scrum Framework on Projects in IT Sector. European Project Management Journal. 2021. Vol. 11 (2). pp. 37–46. DOI: 10.18485/epmj.2021.11.2.4.
8. Morandini M., Coleti T.A., Oliveira E., Corrêa P.L.P. Considerations About the Efficiency and Sufficiency of the Utilization of the Scrum Methodology: A Survey for Analyzing Results for Development Teams. Computer Science Review. 2021. Vol. 39. DOI: 10.1016/j.cosrev.2020.100314.
9. Saeedi K., Visvizi A. Software Development Methodologies, HEIs, and the Digital Economy. Educ. Sci. 2021. Vol. 11 (2). P. 73. DOI: 10.3390/ educsci11020073.
10. Orlov E. V., Rogulenko T. M., Smolyakov O. M., Oshovskaya N. V., Zvorykina T. I., Rostanets V. G., Dyundik E. P. Comparative Analysis of the Use of Kanban and Scrum Methodologies in IT Projects. Universal Journal of Accounting and Finance. 2021. Vol. 9 (4). pp. 693–700. DOI: 10.13189/ ujaf.2021.090415.
11. Hamzah A., Mazni O., Rohaida R. The State of the Art of Agile Kanban Method: Challenges and Opportunities. Independent Journal of Management & Production. 2021. Vol. 12 (8). pp. 2535–2550. DOI: 10.14807/ijmp.v12i8.1482.
12. Alsaqqa S., Sawalha S., Abdel-Nabi H. Agile Software Development: Methodologies and Trends. International Journal of Interactive Mobile Technologies. 2020. Vol. 14(11). pp. 246–270. DOI: 10.3991/ijim.v14i11.13269.
13. Necmettin O., Busra O.K., Mehmet G. Towards a Better Understanding of Agile Mindset by Using Principles of Agile Methods. Proceedings of the Federated Conference on Computer Science and Information Systems. 2020. Vol. 21, pp. 721–730. DOI: 10.15439/2020F46.
14. Jayakody J.A.V.M.K., Wijayanayake J. Challenges for Adopting Devops in Information Technology Projects. International Research Conference on Smart Computing and Systems Engineering (SCSE), Colombo, Sri Lanka. 2021. pp. 203–210. DOI: 10.1109/SCSE53661.2021.9568348.
15. Angara J., Prasad S., Gutta S. DevOps Project Management Tools for Sprint Planning, Estimation and Execution Maturity. Cybernetics and Information Technologies. 2020. Vol. 20 (2). pp. 79–92. DOI: 10.2478/cait-2020-0018.
16. Akbar M.A., Khan A.A., Islam N., Mahmood S. DevOps Project Management Success Factors: A Decision-Making Framework. Softw: Pract Exper. 2024. Vol. 54 (2). pp. 257–280. DOI: 10.1002/ spe.3269.

Abstract

This paper presents a system of metrics for the automated evaluation of university-level academic courses, based on the analysis of topic formulations and their structural organization. The objective of the study is to develop a formalized quantitative methodology for assessing course quality, accounting for both internal content characteristics and alignment with a reference syllabus (when available). The proposed approach includes reference-independent metrics that assess logical coherence, structural balance, and content redundancy, as well as reference-based metrics that evaluate topic coverage and thematic relevance. Analytical tools include graph-based representations, semantic embeddings, and sequence-based metrics. The methodology was tested on a corpus of mathematics-related university courses. The results demonstrate the system’s effectiveness in identifying logical inconsistencies, missing core topics, and irrelevant content. The proposed approach can be used to support the design and revision of academic courses and to provide automated feedback in digital learning environments. As a next step, the integration of the system with generative language models and the extension of the metric set to include content-level characteristics—such as topic depth and complexity levels—is being considered.

Keywords: automated evaluation, course generation, course structure, graph representation, reference-based metrics, reference-free metrics, semantic relevance, subject alignment, syllabus comparison, topic formulations

References:

1. Shalkina T. N. A Model for Comprehensive Evaluation of the Quality of Electronic Educational Resources. Pedagogicheskaya informatika. 2013. No. 4. pp. 85–91. (In Russ.).
2. Starygina S. D., Nuriev N. K., Garifyanov N. F. Evaluation of Course Quality: Model Development, Automation of Calculations, Implementation Examples. Obrazovatel’nye tekhnologii i obshchestvo. 2018. Vol. 21. No. 2. pp. 390–405. (In Russ.).
3. Starygina S. D., Nuriev N. K., Nurgalieva A. A. Quantitative Assessment of Online Course Quality. Informatika i Obrazovanie. 2018. No. 7. pp. 68–76. (In Russ.).
4. Bogdanova A. V., Glazova V. F., Konovalova E. Yu. Intelligent Technologies for Evaluating the Quality of Distance Learning Courses in Higher Education. Baltiiskii gumanitarnyi zhurnal. 2017. Vol. 6. No. 1 (18). pp. 82–86. (In Russ.).
5. Chen X., Xie H., Tao X., Wang F. L., Cao J. Leveraging Text Mining and Analytic Hierarchy Process for the Automatic Evaluation of Online Courses. International Journal of Machine Learning and Cybernetics. 2024. Vol. 15. No. 11. pp. 4973–4998. DOI:10.1007/s13042‑024‑02203‑6.
6. Yuan B., Hu J. An Exploration of Higher Educa-tion Course Evaluation by Large Language Models. 2024. DOI: 10.48550/arXiv.2411.02455.
7. Minnegalieva Ch. B. Some Problems of Using Distance Learning Technologies. Informatsionnye tekhnologii i sistemy. 2013. No. 1 (70). pp. 54–58. (In Russ.).
8. Erbisti B., Marzagão D.K., Braganholo V. An Extension to Kendall’s Tau Metric to Evaluate Dissimilarities Between Data Series. Journal of the Brazilian Computer Society. 2024. Vol. 30. No. 1. Available at: https://journals-sol.sbc.org.br/index.php/jbcs/article/view/2803

9. Wang S., Xu T., Li H., Zhang C., Liang J., Tang J., Yu P.S., Wen Q. Large Language Models for Education: A Survey and Outlook. arXiv preprint arXiv:2403.18105. 2024. Available at: https://arxiv.org/abs/2403.18105