Peter the Great St. Petersburg Polytechnic University

This article analyzes the capabilities of modern message brokers in the context of event-driven and scalable architectures of distributed systems. It examines the theoretical foundations of message-exchange systems, including pub/sub models, queues, and stream processing, as well as their role in ensuring reliability and flexibility of inter-service communication. Particular attention is given to the comparative characteristics of two widely used brokers: RabbitMQ and Apache Kafka. It analyzes their architectural principles, message delivery and storage mechanisms, scalability features, fault tolerance, and performance metrics. The applicability of these tools under different workload types and architectural requirements is also investigated.

Synapse loss is one of the main biological correlates of cognitive decline in Alzheimer’s disease (AD), and it occurs long before noticeable neuronal degeneration. Transgenic mice with mutations characteristic of familial forms of AD (FAD) are widely used to model changes in neuronal structure and function. The 5xFAD mouse model is characterized by accelerated formation of the pathogenic amyloid-beta peptide (Aβ) and exhibits pronounced neurodegeneration in the hippocampus and neocortex in the early postnatal period. The 5xFAD model allows studying the mechanisms of AD pathogenesis at all stages of this neurodegenerative disease. We present long-term studies of dendritic spine dynamics revealed using in vivo two-photon microscopy in hybrid mice (5xFAD-M), obtained by crossing 5xFAD mice with the Thy1-EGFP line expressing green fluorescent protein (GFP). Long-term imaging of dendritic segments of pyramidal neurons in layers II–III of the somatosensory cortex revealed a significant reduction in dendritic spine density in 5xFAD-M mice aged 4 to 6.5 months. Dynamic decrease of dendritic spine density is due to a general suppression of their both formation (gain) and elimination (loss), while the number of “stable” spines remains relatively unchanged.

This study presents a hybrid intelligent forecasting and decision-support framework for sustainable urban development based on the Adaptive Neuro-Fuzzy Inference System (ANFIS). The research employs data from 12 major Russian cities, covering environmental, social, and economic indicators across a nine-month period in 2025. The proposed architecture integrates fuzzy logic with neural network learning, enabling adaptive modeling of complex urban dynamics. A hybrid optimization algorithm combining genetic search and backpropagation was used to calibrate model parameters and enhance predictive accuracy. Evaluation through MAPE, RMSE, R², and Theil’s U statistic demonstrated superior performance of the ANFIS model compared to conventional approaches such as Random Forest, ARIMA, and linear regression. The results confirm the effectiveness of hybrid intelligent systems in supporting strategic planning and real-time management in smart city environments, providing a scalable foundation for sustainable urban governance.

This paper addresses the issues related to the implementation of the interaction of automaton objects formalized using specialized state transition graphs. This representation approach, similar to state machine diagrams in Unified Modeling Language, significantly simplifies the development and subsequent maintenance of software. Each automaton object manages specific behavioral aspects of the system while their interaction through the corresponding interfaces ensures the achievement of common goals. Visualization of these objects is implemented using the CIAO (Cooperative Interaction of Automaton Objects) v.3 automata-based programming language. The implementation of the interaction mechanism involves developing a software system that supports the joint execution and interaction of automaton objects. To implement the proposed automaton objects interaction, the bootstrapping technique, known since the mid-1960s, is used. This method involves creating a compiler or interpreter in the same language for which it is being developed. The stepwise refinement method is used to construct the initial interpreter. Subsequently, using transformation patterns from imperative to automata-based constructs, the interpreter is modified into a system of interacting automaton objects, thus achieving the result of the bootstrapping process. This research yielded data structures for representing CIAO v.3 programs. The interpreter’s structure was described in pseudocode using the stepwise refinement method. A set of patterns is proposed to implement imperative constructs through automata-based programming techniques. The structure of the CIAO v.3 language interpreter is presented using CIAO v.3 itself. A Python-based interpreter prototype was realized. The conducted study demonstrates the successful software self-implementation of the CIAO v.3 using the bootstrapping method. The CIAO v.3 language provides efficient design and implementation of software solutions, and also guarantees fault-tolerant component interaction due to the ability to automatically check the properties of CIAO v.3 programs. The proposed approach can be utilized for implementing domain-specific languages in multi-agent systems and human-machine interaction interfaces.

The influence of aluminum contents on the cavitation corrosion resistance of CoCrFeNiAlx high-entropy alloy (HEA) coatings deposited on TC4 via laser cladding was investigated in both distilled water and artificially simulated seawater. The results showed that the cavitation corrosion resistance of the coatings increased and then decreased with increasing Al content in both media. The weight loss rate was significantly higher in simulated seawater than in distilled water. Increasing the Al content led to a transition in the coating phase from a single FCC phase to FCC + BCC dual phases, which improved the corrosion resistance. However, as the BCC phase content increased, the plasticity of the coating decreased while the strength increased, reducing the coating’s ability to absorb cavitation energy and leading to severe cavitation damage.

This study investigates the temperature dependence of ferroelectric domain wall motion in epitaxial PbZr0.3Ti0.7O3 (PZT) thin films (60 nm) using piezoresponse force microscopy (PFM) from 5 to 300 K. Contrary to thermal activation models, domain radii (and inferred domain wall velocities) exhibit minimal reduction at cryogenic temperatures (down to 5 K) compared to room temperature. Suggesting non-thermal mechanisms govern domain dynamics in PZT at low temperatures. Additional studies exclude surface charge accumulation and adsorbed water layers as primary factors, confirmed via destructive 3D polarization mapping showing domain stability throughout the film depth. It is shown that AFM probe tip geometry critically influences domain sizes: Tip blunting or contamination during scanning causes significant artifacts in field distribution, explaining apparent size discrepancies between high- and low-temperature datasets acquired with different probes. However, this effect cannot account for the persistent domain growth kinetics observed at low temperatures. We propose that the formation of a domain wall in a high-magnitude electric field affects its further movement in low-magnitude fields. Our findings highlight probe-geometry control as essential for reliable PFM measurements. The overall conclusion is that in experiments investigating domain wall motion in thin films, when a nonuniform electric field of the AFM probe is used to switch the polarization, it is difficult to obtain results for reliable qualitative and quantitative conclusions due to the large number of unknown parameters.