DOI: 10.33917/mic-3.128.2026.5-11

The article examines the challenges of developing a more effective system for supporting sustainable population growth in the country, with the goal of cultivating a socially active generation capable of resisting hybrid attacks on Russia. It substantiates the need to develop measures aimed at ensuring that the controlled circuit of a digital service system with elements of artificial intelligence in organizational systems for managing the processes of monitoring and stimulating the reproduction of target population categories capable and willing to solve complex defense and civilian tasks is prepared for complex problematic situations and limitations of a natural and artificial nature (focusing on a possible future global military conflict in Europe and Asia). It is concluded that the most appropriate management tool is the use of a digital information and management platform for streamlining the processes and procedures of socio-economic support for an increase in the birth rate of the population (increasing the birth rate, improving health, the institution of marriage, protecting and supporting the family by improving well-being, as well as quality of life) in relation to the demographic supersystem, understood as the most important institutional element of the state. It is proposed to integrate monitoring and management tools on a supercomputer basis. The obtained results will help address the organizational challenge of maintaining governance in the event of demographic indicators «exceeding the limits favorable for defense capability objectives’ by working with sources (development and implementation centers) of strategic teams for socioeconomic support of population growth.

References:

1. Ageev A.I. The Role of Sanctions, Conflicts, and Special Operations: Reflections on the Future. Part 2. Scientific Bulletin of the Defense Industrial Complex of Russia. 2024;1:33-42.

2. Ageev A.I., Grabchak E.P., Loginov E.L. Using Supercomputer Technologies to Manage the Operations of Very Large Organizational Systems in the Implementation of Complex Special Projects (Operations). Microeconomics. 2024;1:5-10.

3. Ageev A.I., Grabchak E.P., Loginov E.L. A New Model of Managing the Russian Economy in the Context of Hybrid Risks and Threats. Economic Strategies. 2025;27(5(203)):6-19.

4. Demography 2030. How to Ensure Sustainable Population Growth in the Russian Federation. Special Report of the Civic Chamber of the Russian Federation / S.I. Rybalchenko, A.V. Korotaev, I.A. Efremov, et al.; ed. by S.I. Rybalchenko; OPRF. Moscow: Pacioli Consulting LLC, 2023. 86 p.

5. Ageev A.I., Grabchak E.P., Loginov E.L. Superposition of Long-Term Demographic Trends on Geopolitical Shocks in a Complex Economic Situation. Microeconomics. 2026;1:5-11.

6. Loginov E.L. Economic Development in Russia under the Impact of Sanctions. Moscow: Rusains, 2024. 198 p.

7. Loginov E.L., Shkuta A.A. Digital Control Centers in the Global Economy: Using Elements of Artificial Intelligence to Manage Complex-Structured Organizational Systems. Moscow: MNIIPU, 2021. 181 p.

DOI: 10.33917/mic-3.128.2026.12-17

The article continues the analysis of China’s technological sovereignty, moving from basic industries to applied sectors. Materially conditioned sectors are considered: smart agriculture, new energy, transport, satellite systems and medical equipment. Additionally, non-materially conditioned sectors are analyzed: educational technologies, blockchain and digital currency, entertainment technologies, social networks and messengers, security technologies, and virtual reality as an integration platform on the intersection of entertainment, education, and human sensory systems. The role of state planning and import substitution is shown.

References:

1. State Council of the PRC. Made in China 2025. URL: https://docs.yandex.ru/docs/view?tm=1780384199&tld=ru&lang=en&name=t0432_made_in_china_2025_EN.pdf

2. National Energy Administration of the PRC. Guiding Opinions on Promoting Integrated and Synergistic Development of New Energy, 2025.

3. China Sensor and Internet of Things Industry Alliance. Sensor Industry White Paper, 2025.

4. Ministry of Agriculture of the People’s Republic of China (2025, December 4). Key technologies promoted for smart agriculture this year: Autonomous operation technology for field agricultural machinery, 2025.

5. Xinhua (2025, July 2). Across China: Unmanned farming paves way for China’s smart, sustainable agriculture future? 2025. URL: China.org.cn

6. Xinhua (2025, October 11). China builds world-leading meteorological observation system during 14th Five-Year Plan period. Global Times, 2025.

7. Xinhua (2025, January 3). China’s meteorological observation service utilizes its first small commercial weather satellites. People’s Daily Online, 2025.

8. International Energy Agency (IEA). 14th FYP Energy Technology Innovation — Policies, 2025. URL: https://www.energyconnects.com/opinion/features/2025/march/iea-new-energy-technologies-need-investments-to-scale-up-innovation/

9. Xinhua (2025, April 29). Economic Watch: Wheels to wings: Chinese automakers challenge frontiers of mobility. URL: China.org.cn

10. Global Times. (2025, April 9). China’s 50kg-class hydrogen-powered drone sets new domestic record with 30-hour continuous flight, 2025. URL: https://www.globaltimes.cn/page/202504/1331772.shtml

11. People’s Daily. (2026, January 5). A look back at a transformative year in China’s auto industry, 2026. URL: https://en.people.cn/n3/2026/0105/c90000–20410243.html

12. CATL. Solid-state battery development roadmap, 2025. URL: https://battery-tech.net/

13. China Energy Storage Alliance (CNESA). (2025, December 15). Industry News, 2025. URL: https://en.cnesa.org/

14. SASAC. (2025, December 4). China Launches Largest Semi-Solid-State Lithium Battery Storage Project, 2025. URL: https://en.cnesa.org/latest-news/2025/12/1/

15. State-owned Assets Supervision and Administration Commission (SASAC). Report on grid-scale vanadium flow battery project, 2025. URL: http://en.sasac.gov.cn/

DOI: 10.33917/mic-3.128.2026.18-35

This article presents an adaptive asset portfolio sales management mechanism designed for financial directors and managers working with distressed assets. The mechanism is based on the stochastic DOAS model [1], which takes into account the time value of money, the probabilistic nature of demand, and the uncertainty of market conditions. A key feature is a closed-loop feedback control loop that allows for automatic adjustment of the model parameters as actual sales data accumulates. The article provides a mathematical description of the model, including the logistic probability function, the calculation of the expected net present value, the Hurwitz criterion for accounting for risk [15], and an algorithm for adapting parameters based on the maximum likelihood method [19]. A demonstration of the mechanism’s operation is provided using real data from a portfolio of 12 real estate properties of three types. Retrospective testing of the adaptive mechanism is conducted. The material is aimed at managers wishing to transfer asset management from the intuitive realm to the realm of quantitative risk management [2, 3].

References:

1. Agalarov Z. S., Pikhtin K. A. Stochastic Model of Managing the Process of Sales of a Portfolio of Property Assets Taking into Account the Time Value of Money and Decision-Making Criteria under Uncertainty. Controlling. 2026;1 (99):32–42.

2. Agalarov Z. S., Polyakov V. M. Multicriteriality in Optimal Investment Planning Problems. Controlling. 2017;4 (66):16–23.

3. Astrakhantsev V. I. Fundamentals of Adaptive Management. St. Petersburg: Lan, 2021. 312 p. ISBN 978-5-8114-7892-1.

4. Brailey R., Myers S., Allen F. Principles of Corporate Finance: textbook / translated from English. 12th ed. Moscow: Dialectika Publishing House, 2020. 1072 p. ISBN 978-5-907114-81-4.

5. Vartanyan K. A. Using Probabilistic-Statistical Methods in Assessing the Degree of Reliability of Market Value Assessment Results. Vestnik otsenki. 2019;2:45–58.

6. Damodaran A. Investment Valuation: Tools and Methods for Valuing Any Assets / translated from English by V. Ionova. 11th ed., revised and enlarged. Moscow: Alpina Publisher, 2025. 1320 p. ISBN 978-5-9614-6650-8.

7. Ermoliev Yu. M. Stochastic Models and Methods in Economic Planning. Moscow: Nauka, 1983. 328 p.

8. Kalnitskaya I. V. Liquidity of an Organization’s Assets: Assessment Problems and Directions for Their Solution. Financial Management. 2024;3:22–35.

9. Kuzmenko I. P. Adaptive Management as a Tool for Improving the Stability of Economic Entities. Economics and Management. 2011;4:89–95.

10. Makshanov A. V., Musaev A. A. Stochastic Modeling: A Textbook for Universities. St. Petersburg: Lan Publishing House, 2021. 128 p.

11. Mirzamurodov O. S. Financial Risk Assessment Using Stochastic Modeling and the Monte Carlo Method. Economics and Management. 2025;1:67–78.

12. Neumann J. von, Morgenstern O. Game Theory and Economic Behavior / translated from English; edited and with additions by N. N. Vorobyov. Moscow: Nauka, Main Editorial Board of Physics and Mathematics Literature, 1970. 708 p.

DOI: 10.33917/mic-3.128.2025.36-45

The article aims to provide a theoretical foundation and conceptual operationalization of a system of digital metrics for evaluating the effectiveness of project teams in a hybrid environment. By analyzing current research on the impact of remote and hybrid work on employee productivity, coordination, and well-being, and supplementing it with a content analysis of the functionality of common digital project management and work monitoring tools, the article proposes a classification of metrics and an integrated digital index for evaluating the effectiveness of hybrid teams. Existing digital indicators primarily measure activity and workload, while the quality of collaboration, role distribution, and cognitive load are only partially addressed. The article reveals the limitations and risks of widespread use of digital metrics: distortion of employee behavior, increased digital control, and possible discrimination.

References:

1. Yang L., Holtz D., Jaffe S. et al. The effects of remote work on collaboration among information workers. Nature Human Behavior. 2022;6 (1):43–54.

2. Šmite D., Tkalich A., Moe N. B. et al. Changes in perceived productivity of software engineers during COVID-19 pandemic: The voice of evidence. Journal of Systems and Software. 2022. Vol. 186. Article 111197.

3. Gibbs M., Mengel F., Siemroth C. Work from Home and Productivity: Evidence from Personnel and Analytics Data on Information Technology Professionals. Journal of Political Economy Microeconomics. 2023;1 (1):7–41.

4. Siegel R., König C. J., Lazar V. The impact of electronic monitoring on employees’ job satisfaction, stress, performance, and counterproductive work behavior: A meta-analysis. Computers in Human Behavior Reports. 2022. Vol. 7. Article 100227.

5. Aloisi A., De Stefano V. Essential jobs, remote work and digital surveillance: Addressing the COVID-19 pandemic panopticon. International Labor Review. 2022;161 (2):289–314.

6. De Stefano V., Wouters M. AI and digital tools in workplace management and evaluation: An assessment of the EU’s legal framework. Osgoode Legal Studies Research Paper, 2022.

7. Maity R. The impact of remote and hybrid work models on small and medium-sized enterprises’ productivity: A systematic literature review. SN Business & Economics. 2025. Vol. 5. Article 158.

8. Kapelyushnikov R. I., Zinchenko D. I. Digital Forms of Employment in the Russian Labor Market. Part I: Remote Employment. Public Opinion Monitoring: Economic and Social Changes. 2024;6:157–181.

9. Konovalova V. G. Hybrid Work Model: Pro et Contra. Human Resources and Intellectual Resource Management in Russia. 2024;3 (72):24–30.

10. Özkan D., Mishra A. Agile Project Management Tools: A Brief Comparative View. Cybernetics and Information Technologies. 2019;19 (4): 17–25.

11. Milojević D., Macuzic I., Djordjevic A., Savković M., Djapan M. Comparative analysis of software tools for agile project management. Proceedings of the 14th International Quality Conference, 2023.

DOI: 10.33917/mic-3.128.2026.46-57

The article analyzes the essence of innovation activity as a complex indicator reflecting not only efficiency (for example, the number of new products or technologies introduced), but also the intensity of the processes of innovation search, development and commercialization. The indicators of Russia in the Global Innovation Index are considered, the main directions of their increase are identified — financing innovative developments, as well as increasing income from innovation activity. The relationship between the level of innovative activity of an enterprise and the key parameters of its competitiveness is shown: productivity, market share, resistance to external changes and the ability to create unique consumer values. Practical recommendations are proposed for the management of companies and government agencies to stimulate innovation as a basis for increasing competitiveness.

References:

1. Vlasova V. V. Global Innovation Index — 2025. Moscow: ISIEZ HSE. URL: https://issek.hse.ru/news/1085304545.html

2. Voskresenskaya O. V. The innovative potential of Russia, its regions and industries. Bulletin of the Altai Academy of Economics and Law. 2024;11–1:17–24.

3. Porter M. Competition [trans. from the English by O. L. Pelyavsky]. 3rd ed. Moscow: Alpina Business Books: Company XXI century, 2005. 610 p.

4. Korsunov P. P. Methodological tools for assessing and planning the competitiveness of enterprises of the fuel and energy complex / 08.00.05 — dissertation of the Candidate of Economic Sciences. Ural Federal University named after the first President of Russia B. N. Yeltsin. Yekaterinburg, 2018. 202 p.

5. Makarova E. P. Innovative agricultural centers and parks in the USA. Creative economy. 2015;9 (3):407–420.

6. Mesinova I. A. On the ambiguity of assessing the impact of a country’s participation in international technological exchange on the level of its competitiveness / I. A. Mesinova, Zh. B. Amirkhanyan. Issues of innovative economics. 2025;15 (1):13–24. DOI 10.18334/vinec.15.1.122316

7. Moroshkina A. Results of the global innovation index. URL: https://trends.rbc.ru/trends/innovation/633d4d369a7947fc5a8af5cb

8. Science and innovation. Moscow: International State Statistics Service, URL: https://www.rosstat.gov.ru/statistics/science

9. Petrova V. Russia has been short of innovation. URL: https://www.kommersant.ru/doc/7183505

10. Pechkovsky I. M. Introduction of equipment failure prediction systems in the field of mechanical engineering. Modern high-tech technologies. 2024;2:165–170. URL: https://na-journal.ru/2–2024-informacionnye-tekhnologii/

11. Indicators of the digital economy 2025: a statistical collection. Moscow: Rosstat, 2025. 292 p. URL: https://rosstat.gov.ru/folder/154849?print=1

12. Startsev P. V. Analysis of approaches to the essence of the concepts of «enterprise competitiveness» and «competitive advantage». Russian entrepreneurship. 2014;15 (16):4–15.

13. Tataeva I. Y. International supply chain. Series. 30 years of the Russian Customs Academy. 1993–2023. Moscow, 2023.

DOI: 10.33917/mic-3.128.2026.58-64

The article focus is on the social determinants of transformation, which determine the change in reputation strategies of companies and social feelings of workers. The psychological and social problems of the working-age population are analyzed as one of the possible consequences of transformational processes in the labor market in view of increasing inequality risks and social vulnerability of individual categories of workers. The work uses methods of comparative and structural analysis, generalization of official statistics data (VCIOM), as well as professional communities. Based on the conducted research, conclusions are drawn regarding the specifics of the Russian labour market’s adaptation to the digital age challenges and recommendations are formulated for socially oriented policies aimed at mitigating the negative consequences and maximizing the digital transformation benefits for the society. Several scientific analysis methods were used: method of analysis and synthesis, abstraction, description, induction, deduction.

References:

1. National Social Reputation Rating: Russian Business as Perceived by Russians. Official website of VTsIOM. 2025. URL: https://wciom.ru/presentation/prezentacii/nacionalnyi-reiting-socialnoi-reputacii-rossiiskii-biznes-v-vosprijatii-rossijan-18–12

2. More than 90 % of companies face staff shortages. What to expect in the labor market in 2025. Moscow Employment Center «My Work». 2025. URL: https://czn.mos.ru/ER/News/NewsCard/549?_=Bolee-90-kompanij-stalkivayutsya-s-nehvatkoj-sotrudnikov-Chego-ozhidat-na-rynke-truda-v-2025-godu

3. 74 % of specialists consider a company’s reputation when hiring. Online publication «CNews». 2025. URL: https://www.cnews.ru/news/line/2025-08-08_74_spetsialistov_uchityvayut#

4. Review of best global practices in corporate employee well-being programs. National Research University «Higher School of Economics». 2024. 24 p. URL: https://wec.hse.ru/mirror/pubs/share/936482764.pdf

5. In Search of Meaning: How Generation Z is Changing Culture in the Labor Market. Moscow Employment Center «My Work». 2025. URL: https://czn.mos.ru/EE/News/NewsCard/1089?_=Vpoiskah-smysla-kak-pokolenie-Z-menyaet-kulturu-na-rynke-truda

6. Erikson E. Identity: Youth and Crisis: A Textbook / E. Erikson, general editor and foreword by A. V. Tolstykh, translated from English [Andreeva, A. D., et al.]. 2nd ed. Moscow: Flinta [et al.], 2006. 341 p.

7. Kochneva E. M., Blokhina M. M. The Crisis of Professional Identity of Young Professionals. The World of Science. Pedagogy and Psychology. 2020. No. 3. 10 p. URL: https://mir-nauki.com/PDF/71PSMN320.pdf

8. Digital Skills Training: Global Challenges and Best Practices. Analytical Report for the III International Conference «More Than Training: How to Develop Digital Skills». Sberbank Corporate University. Moscow: ANO DPO «Sberbank Corporate University». 2018. 122 p. URL: https://sberuniversity.ru/upload/iblock/2f8/Analytical_report_digital_skills_web_demo.pdf