In modern science, the processes of transferring material, information, and legal objects are often considered in isolation, which prevents the identification of universal patterns of their functioning and complicates the assessment of aggregate transfer costs in heterogeneous systems. The aim of this work is to develop a unified mathematical apparatus for describing and optimizing the processes of transition of entities of various natures between agents. The research is based on the methods of systems analysis and mathematical modeling, within which a classification of structural similarities (isomorphisms) in logistical, legal, and information processes was carried out. To formalize environmental influences, a method of grouping factors into nine basic parameter transformation mechanisms was used: time, cost, reliability, and structural complexity. As a result of the work, a universal model was created that allows for the quantitative assessment of environmental resistance through the apparatus of binary sensitivity flags. A two-stage optimization algorithm has been developed, implementing preliminary filtering of trajectories by a calculated reliability threshold and a subsequent search for the minimum of the aggregate cost objective function. The practical significance of the results lies in the possibility of detecting hidden resource losses and time lags that are not captured by traditional highly specialized analysis methods. The proposed approach allows for the design of resilient transfer systems, ensuring a balance between the speed of entity transmission and process safety in a dynamically changing environment.
1. Kabashkin I., Sansyzbayeva Z. Methodological Framework for Sustainable Transport Corridor Modeling Using Petri Nets. Sustainability. 2024;16(2). https://doi.org/10.3390/su16020489
2. Zhou J., Qin X., Ding Y., Ma H. Spatial-Temporal Dynamic Graph Differential Equation Network for Traffic Flow Forecasting. Mathematics. 2023;11(13). https://doi.org/10.3390/math11132867
3. Goudey B., Geard N., Verspoor K., Zobel J. Propagation, detection and correction of errors using the sequence database network. Briefings in Bioinformatics. 2022;23(6). https://doi.org/10.1093/bib/bbac416
4. Hussain A., Hussain T., Attar R.W., et al. Energy-efficient synchronization for body sensor network in the metaverse: an optimized connectivity approach. EURASIP Journal on Wireless Communications and Networking. 2025;2025(1). https://doi.org/10.1186/s13638-025-02433-4
5. Li B., Kong L., Zhang X., et al. Deep Learning-Based Secure Transmission Strategy with Sensor-Transmission-Computing Linkage for Power Internet of Things. Computers, Materials & Continua. 2024;78(3):3267–3282. https://doi.org/10.32604/cmc.2024.047193
6. Mitrea D.A., Marian C.V., Manolescu R.A. Digital Transformation: Design and Implementation of a Blockchain Platform for Decentralized and Transparent Property Asset Transfer Using NFTs. World. 2025;6(4). https://doi.org/10.3390/world6040166
7. Hodgson G.M. Transactions and legal institutionalism: part I – six leading thinkers. Journal of Institutional Economics. 2025;21. https://doi.org/10.1017/S1744137425000049
8. Xue Y., Liu H., Chai Zh., Wang Z. The Decision-Making and Moderator Effects of Transaction Costs, Service Satisfaction, and the Stability of Agricultural Productive Service Contracts: Evidence from Farmers in Northeast China. Sustainability. 2024;16(11). https://doi.org/10.3390/su16114371
9. Fang F., Ma J., Ma Y.-J., Boccaletti S. Social contagion on higher-order networks: The effect of relationship strengths. Chaos, Solitons & Fractals. 2024;186. https://doi.org/10.1016/j.chaos.2024.115149
10. Ali M. Dynamic graph models for evolving social networks. Journal of Mathematical Problems, Equations and Statistics. 2024;5(1):93–99. https://doi.org/10.22271/math.2024.v5.i1a.249
11. Zhang M., Liu L., Wang Y. Research on the dynamic spread of information in social networks based on relationship strength theory and feedback mechanism. Frontiers in Physics. 2024;12. https://doi.org/10.3389/fphy.2024.1327161
12. Dong Y., Huo L., Perc M., Boccaletti S. Adaptive rumor propagation and activity contagion in higher-order networks. Communications Physics. 2025;8(1). https://doi.org/10.1038/s42005-025-02181-3
13. Zhuravlev M.V., Ashmarina T.I., Vakhrusheva I.A., Muzalev K.S. Modeling of heat flows in underground mine workings using parallel programming. News of the Tula State University. Sciences of Earth. 2025;(3):292–297. (In Russ.).
14. Kukartsev V.V., Tynchenko V.S., Chzhan E.A., et al. Solving the problem of trucking optimization by automating the management process. Journal of Physics: Conference Series. 2019;1333(7). https://doi.org/10.1088/1742-6596/1333/7/072027
15. Matania O., Klein R., Bortman J. Transfer Across Different Machines by Transfer Function Estimation. Frontiers in Artificial Intelligence. 2022;5. https://doi.org/10.3389/frai.2022.811073
16. Yan Zh., Sun J., Zhang Y., et al. Federated Transfer Learning Strategy: A Novel Cross-Device Fault Diagnosis Method Based on Repaired Data. Sensors. 2023;23(16). https://doi.org/10.3390/s23167302
17. Wang W., Li Zh., Li W. Graph embedding-based heterogeneous domain adaptation with domain-invariant feature learning and distributional order preserving. Neural Networks. 2024;170:427–440. https://doi.org/10.1016/j.neunet.2023.11.048
18. Mu Z., Li D., Zhao J., Jiang H., Shao Q. TransNet: A transfer-augmented domain adaptation model for cross-domain water quality index prediction in data-scarce scenarios. Knowledge-Based Systems. 2025;328. https://doi.org/10.1016/j.knosys.2025.114220
19. Boyko A.A., Kukartsev V.V., Eremeev D.V., et al. The dynamic simulation model of calculating equipment purchase with the bond loan. Journal of Physics: Conference Series. 2019;1399(3). https://doi.org/10.1088/1742-6596/1399/3/033120
20. Si H., Li W., Wang Q., et al. A secure cross-domain interaction scheme for blockchain-based intelligent transportation systems. PeerJ Computer Science. 2023;9. https://doi.org/10.7717/peerj-cs.1678
21. Jia L., Zhang Q., Liu Sh., Kong B., Liu Y. Multi-Station Agricultural Machinery Scheduling Based on Spatiotemporal Clustering and Learnable Multi-Objective Evolutionary Algorithm. AgriEngineering. 2025;7(6). https://doi.org/10.3390/agriengineering7060197
22. Sun Y., Tian Zh. Solving few-shot problem in wind speed prediction: A novel transfer strategy based on decomposition and learning ensemble. Applied Energy. 2025;377(5). https://doi.org/10.1016/j.apenergy.2024.124717
23. Cui T., Shi Y., Wang J., et al. Practice of an improved many-objective route optimization algorithm in a multimodal transportation case under uncertain demand. Complex & Intelligent Systems. 2025;11(2). https://doi.org/10.1007/s40747-024-01725-4
24. Bukhtoyarov V.V., Tynchenko V.S., Petrovsky E.A., Dokshanin S.G., Kukartsev V.V. Research of methods for design of regression models of oil and gas refinery technological units. IOP Conference Series: Materials Science and Engineering. 2019;537(4). https://doi.org/10.1088/1757-899X/537/4/042078
25. Xu Zh., Zheng Ch., Zheng Sh., Ma G., Chen Zh. Multimodal Transportation Route Optimization of Emergency Supplies Under Uncertain Conditions. Sustainability. 2024;16(24). https://doi.org/10.3390/su162410905
26. Lemus-Romani J., Crawford B., Cisternas-Caneo F., Soto R., Becerra-Rozas M. Binarization of Metaheuristics: Is the Transfer Function Really Important? Biomimetics. 2023;8(5). https://doi.org/10.3390/biomimetics8050400
27. Liu Y., Ma C., Huang Y. An Internet of Things-Based Production Scheduling for Distributed Two-Stage Assembly Manufacturing with Mold Sharing. Machines. 2024;12(5). https://doi.org/10.3390/machines12050310
28. Lingyun G., Niffenegger M., Jing Zh. A novel procedure to evaluate the performance of failure assessment models. Reliability Engineering & System Safety. 2022;226(3). https://doi.org/10.1016/j.ress.2022.108667
29. Shang Y., Nogal M., Teixeira R., Wolfert A.R.M. Extreme-oriented sensitivity analysis using sparse polynomial chaos expansion. Application to train-track-bridge systems. Reliability Engineering & System Safety. 2023;243(1). https://doi.org/10.1016/j.ress.2023.109818
