Modern Transportation Systems and Technologies

The digital transformation of the transport and logistics industry, the growth of international trade, and the increasing complexity of global supply chains necessitate a transition to the concept of smart logistics hubs (SLH), integrating physical and digital infrastructure into a unified, self-regulating complex. The aim of this review is to analyze the technological and organizational-economic foundations of SLH functioning, to generalize the global experience of their implementation, and to identify opportunities for applying this concept in the Russian transport and logistics system. The source base comprised scientific publications by Russian and foreign authors (2020–2025), analytical reports of international organizations, and data on implemented infrastructure and digital projects in the logistics sector; methods of systemic and comparative analysis, content analysis of scientific and applied sources, and a structural-functional approach were applied. The study systematizes key technological drivers of SLH (artificial intelligence, internet of things, digital twin, robotics, autonomous transport), summarizes the international experience of EU countries, China, and Middle East and North Africa (MENA) countries, and identifies constraints of the Russian transformation (infrastructure wear and tear, high capital intensity, fragmented digital integration, regulatory gaps, socioeconomic risks) along with drivers of its acceleration (e-commerce market growth, state support for international transport corridors, regional investment projects). The scientific novelty of the work consists in the development of an original “challenge–implementation direction” correspondence matrix applied to Russian conditions, in the identification of a dual-track SLH development model (modernization of existing nodes and construction of new high-tech facilities), and in the synthesis of an empirical evidence base from 2024–2026 covering unmanned logistics corridor projects and the national digital transport and logistics platform GosLog. It is concluded that the implementation of the SLH concept in Russia requires an integrated approach combining the modernization of fixed assets of transport infrastructure, standardization of digital platforms, the formation of “smart logistics zones” with an experimental legal regime, and the development of human capital in the field of artificial intelligence.

Aim: This article presents an adaptive STSMO (Super-Twisting Sliding Mode Observer) algorithm for a permanent magnet synchronous motor (PMSM) control system, developed to eliminate key drawbacks of conventional approaches: chattering, low observation accuracy, and insufficient robustness. To mitigate the high-frequency chattering caused by the sign function in the sliding mode observer, an adaptive back-EMF filter is proposed. To regulate the influence of the sliding mode gain coefficient on stability in the STSMO algorithm, where the threshold function is difficult to obtain, fuzzy logic control is applied. Based on this, a sensorless control strategy for the PMSM has been developed.

Methods: Mathematical and simulation modeling were performed in the MATLAB environment.

Results: Simulation in MATLAB confirms that the proposed scheme not only ensures high control accuracy but also effectively suppresses the oscillatory effect.

Conclusion: The adaptive STSMO algorithm–by directly improving observation accuracy–ensures more precise functioning of the automatic control system.

Aim: To select materials to create a binder formulation in which the components most effectively minimize friction in heavy-duty joints. The developed lubricant is expected to extend the service life and reliability of such units compared to existing alternatives.

Methods: A comparative analysis of different combinations of materials that reduce friction in contact friction was performed, along with the selection of effective solutions based on benchmarking against existing analogs.

Results: The study substantiates the composition of a multicomponent colloidal lubricant based on a dispersion medium of 90/10 grade petroleum bitumen (which provides adhesion, hydrophobicity, and anti-friction properties), using highly dispersed silicon dioxide as the dispersed phase (which forms a structural framework and exhibits thermal stability), as well as GS4 grade graphite and molybdenum disulfide (which enhance anti-wear characteristics and increase load-carrying capacity). It is shown that the combination of components produces a synergistic effect, ensuring material performance at temperatures up to 400°C and under high mechanical loads. A methodology for selecting component proportions using mathematical models has been developed. The results provide a scientific foundation for the development of next-generation lubricants that meet modern industrial requirements.

Conclusion: The proposed approach can significantly extend the service life of heavily loaded friction units, reduce energy consumption for mechanism operation, and broaden the effective temperature range of lubricants, while also ensuring the environmental safety of production processes through the use of readily available and safe components.

Aim: To assess the quality of answers generated by modern neural network models when determining the type of repair work on bridges and to verify their applicability for engineering decision support.

Methods: Based on the Russian regulatory framework, an original decision matrix was developed linking categories of technical condition, results of load-carrying capacity verification, and recommended types of work (routine repair, major repair, reconstruction). A unified engineering query on the criteria for choosing the type of work was posed to six popular AI models; their answers were qualitatively analyzed using three engineering markers: the role of load-carrying capacity verification, consideration of foundations and substructures, and the application of a risk-based approach. The quality of AI-generated answers is treated as a combination of three components: completeness (coverage of all necessary input data), relevance (compliance with current standards and the bridge management context), and engineering reliability (correctness of the recommended type of work from a risk perspective). Additionally, a cost–quality aspect was examined, taking into account the approximate cost of generating detailed answers.

Results: Neural network models are effective in structuring input data, selecting appropriate terminology, and retrieving regulatory references; however, they do not provide a reliable independent choice of the type of work in cases of incomplete data on foundations and the absence of verification calculations.

Conclusion: The results indicate that neural networks should be used as an auxiliary tool in engineering practice, with mandatory expert involvement and with comprehensive calculation data available; the comparison by the criteria "load-carrying capacity – foundations – risk – cost/quality" shows that low-cost models are suitable for quick text drafts, whereas tasks with strict regulatory requirements justify the use of more expensive models despite the increase in the approximate cost of a single detailed answer. The conclusions are compared with current approaches to digital twins and AI-supported management and decision-making.

Background: Industrial transport is crucial to enterprise economics. It ensures the movement of products throughout all processing stages, while simultaneously forming a separate economic unit that acts as a cost center.

The search for the most effective solutions for industrial logistics is driven by economic, technological, and regulatory factors. Economic factors include the unit costs of industrial transport in the cost of production; technological factors include the consistency of the integrated production process; and regulatory factors include safety and environmental friendliness.

Modern trends in the automation of production and logistics processes favor the implementation of unmanned solutions in industrial transport. With the development of Russian industry, mineral extraction, and the development of new transport corridors, a steady increase in demand for innovative logistics solutions in these areas is expected. This opens up opportunities for modern national solutions, including those based on magnetic levitation (maglev) technology.

Aim: To identify areas of application of maglev technology in industrial transport and to formulate a set of corresponding solutions.

Methods: The research methodology is based on an analysis of the technical and economic characteristics of transport modes, an analysis of industrial logistics processes, and an economic analysis. The research base draws on publicly available information from international non-profit organizations, scientific and educational institutions, and commercial enterprises.

Results: A list of industrial maglev transport solutions has been defined and a preliminary economic assessment has been carried out.

Conclusion: The study confirms that industrial maglev transport has potential for implementation due to its technological and economic advantages over traditional solutions. The implementation of industrial maglev transport should be carried out taking into account the limitations inherent in all types of industrial transport and rely on the possibility of creating effective combined solutions.