Herald of Advanced Information Technology
Intelligent temporal analysis of surface air temperature series for hydrometeorological monitoring
Relevance: The ability to forecast weather conditions is essential for societal development, as it helps minimize damage caused by weather anomalies. At specific points in time, the state of environmental parameters is recorded by a hydrometeorological monitoring system. One of the parameters recorded is surface air temperature. Trends in changes to the current state of the natural environment cannot be determined without knowledge of its previous state and a model of meteorological processes. Therefore, the collection and intelligent analysis of data on the dynamics of temperature processes for subsequent refinement of the meteorological process model remains a relevant task. Aim and research objectives: To conduct an intelligent analysis of long-term time series of surface air temperature to obtain information on the temporal characteristics of surface air temperature variability. To assess changes in temperature trends, temperature trend patterns, and the scales of temperature variability processes. Methods: Time series analysis method. Scientific novelty: A temporal analysis was performed on long-term time series of surface air temperature observations not previously presented in research: temperature changes, the scales of these changes, and trends in temperature variability were assessed. Practical significance: The use of the obtained data in hydrometeorological monitoring systems to refine models of meteorological processes, improve the accuracy of identifying patterns in climate dynamics, and support forecasting decisions. Results: For long-term temperature time series with different properties (different lags, different durations), amplitude characteristics of air temperature changes and temperature trends were obtained, and the scales of temperature change were estimated. Conclusions: As a result of the study, changes in temperature patterns, temperature trends, and the scales of temperature variability processes were assessed over twenty years (from two thousand and five to two thousand and twenty-four) and over one hundred forty-four years (from one thousand eight hundred and eighty-one to two thousand twenty-four) based on an intelligent analysis of four surface air temperature observation series with lags of one year, one month, one day, and three hours.
Information technologies in socio-economic, organisational and technical systems
Information technology in socio-economic,organisational and technical systems
Copyright (c) 2026 Herald of Advanced Information Technology
Multi-criteria selection of the specialist for task execution in the software development process
The object of this study is a process of selecting executors for tasks in software development among the available specialists. The problem being solved is the insufficient accuracy and explainability of existing approaches to selecting a specialist caused by the incompleteness of the characteristics of specialists, in particular soft skills. This can lead to delays in delivery, insufficient quality of delivery, and inefficient use of labor resources in the software development process. The article proposes an information technology for selecting an executor, which is based on a specialist model and a method for selecting an executor for a task. The specialist model takes into account the historical dynamics of possession of technical and social-communicative skills, the success of performing previous tasks, domain expertise, roles in projects, and the context of their execution. The key feature of the developed technology is the formalized representation of the specialist's profile in the form of a structured tuple with classifiers of competence and skill levels, which allows for a quantitative assessment of compliance with task requirements. The model includes seven main components: personal data, education, current role, role history, technical skills, domain knowledge, and soft skills. For each characteristic category, separate classifiers have been developed, which are converted into numerical values for calculations. Based on the created model, a method for evaluating the compliance of a labor resource to a task has been built, which takes into account the weight coefficients of requirements, normalizes the values of characteristics, and provides an explainable compliance metric. The method implements a four-step process: determining the characteristics necessary for task execution; representing specialists in terms of their compliance with task requirements; computing the compliance function with weight coefficients; searching for the best candidates among available specialists. The technology allows automating the process of selecting specialists, taking into account both the current state and the development of competencies over time. Experimental evaluation demonstrated a 41% reduction in decision-making time compared to the traditional approach. The results are explained by the use of a clear structure of characteristics, mathematical formalization of the compliance function, and validation under real operating conditions. The technology can be effectively used in organizations provided there are historical data on the activities of specialists that are guaranteed to be reliable, the dynamics of competence and skill development, and sufficient detail of requirements for the tasks being performed. The technology limitations are the dependence on the reliability of the input data and its inexpediency for use for tasks with an expected effort of less than one man-week.
executor selection information technology
Сoordinated decision support for it projects using multi-agent artificial intelligence systems
Relevance: The article examines adaptive resource allocation and task scheduling in information technology project management under stochastic uncertainty. Centralized planning methods often become unstable when resource availability, task priorities or project scope change during execution. Aim: The aim of the article is to develop and evaluate a coordinated decision support framework for dynamic information technology projects based on a risk-aware multi-agent architecture. Objectives: The study focuses on formalizing the interaction between Project Manager, Task, Resource and Risk agents; defining a utility-based task allocation mechanism; and evaluating the proposed approach under resource disruption and scope creep scenarios. Methods: The proposed system uses a decentralized architecture in which autonomous agents negotiate task assignments through a modified Contract Net Protocol and an auction-based coordination mechanism. The utility function combines competence matching, execution cost, accumulated fatigue and risk probability. The weighting coefficients are selected through an expert pairwise comparison procedure based on the Analytic Hierarchy Process. The experimental evaluation was performed using one thousand Monte Carlo simulation iterations on an adapted project scheduling dataset. Scientific novelty: The novelty of the study lies in combining risk-aware and fatigue-aware decision logic within multi-agent negotiation for information technology project management. Unlike approaches focused only on time or cost, the proposed framework explicitly considers human workload and assignment risk. Practical significance: The framework can support project managers in adaptive re-planning, workload balancing and proactive risk mitigation while preserving the interpretability of task allocation decisions. Results: The proposed system achieved a mean project duration of one hundred twenty-four days, compared with one hundred forty-two days for the static critical path baseline, one hundred thirty-five days for the greedy heuristic and one hundred twenty-eight days for the deep reinforcement learning-based scheduler. The standard deviation of project duration decreased to six point two days, and the resource utilization Gini coefficient decreased to zero point one eight. Conclusions: The results confirm that decentralized risk-aware multi-agent coordination improves project resilience, schedule predictability and workload balance in dynamic information technology project environments.
information technology project management
A method for B2B customer classification based on theutility-weighted temporal profile in E-commerce systems
Purpose: The study addresses the problem of predictive content personalization in business-to-business e-commerce systems, where classical recommendation and segmentation approaches demonstrate limited effectiveness due to the financial heterogeneity, cyclic purchasing behavior, and long-term nature of customer interactions. The purpose of the research is to develop a method for classifying business-to-business customers based on a utility-weighted temporal customer profile, which makes it possible to generate more relevant and timely predictive offers. Objectives: The proposed method is designed as the third stage of an analytical pipeline that extends utility pattern mining and temporal analysis of stable purchasing cycles. Methods: At the first stage, financially significant product patterns are identified using the Utility Pattern Growth approach. At the second stage, the temporal stability of repeated purchases is assessed through inter-purchase time intervals and the coefficient of variation. At the third stage, the obtained pattern-level results are aggregated into an integrated customer profile that combines the average purchasing cycle, cycle stability, and average transaction value. These features are normalized and used for customer clustering, after which customer classes are integrated into the mechanism for determining an individual communication trigger window. Results: The experimental validation was performed on historical transactional and behavioral data from the business-to-business e-commerce platform “Baza Vzuttya” for the period from two thousand twenty-two to two thousand twenty-five. The comparative experiment included four recommendation scenarios: a baseline frequency-based approach, a utility-weighted approach, a utility-temporal approach, and an integrated approach with customer classification. The results show a consistent increase in recommendation conversion from three point nine one percent in the baseline scenario to fifteen point nine one percent in the integrated scenario. At the same time, the number of generated recommendations decreased from nine hundred and twenty to two hundred and twenty, which indicates a reduction in irrelevant communications. The integrated scenario achieved the highest average revenue per recommendation, amounting to six thousand nine hundred and nine point zero nine hryvnias. Conclusions: The obtained results confirm that the proposed method improves the accuracy, timeliness, and economic validity of personalized predictive offers in business-to-business e-commerce systems.
Applied heuristic information technologies for automating power system management in mining and metallurgical complexes
This paper addresses the problem of constructing a knowledge base for an expert system to ensure the quality of the post-contingency state of an electric power system based on the use of heuristic methods of dispatch control. The aim of the article is to study a potential approach to constructing the knowledge base of an expert system to support the decisions of dispatch personnel during the management of the post-contingency state of an electric power system. This study proposes developing the knowledge base using the sensitivity matrix of the controlled operating parameters. The research methodology relies on the principles of the theories of electrical networks and power systems, automatic control, experimental design, artificial intelligence systems, and mathematical statistics. As a result of experimental design, dependencies linking the parameters of the normal pre-contingency state and the network structure with the optimal magnitudes of control actions were obtained. The scientific novelty lies in the integration into a single knowledge base of dispatch operational guidelines during the elimination of emergency states, as well as the coefficients of the sensitivity matrix obtained from computational experiments, taking into account the configuration of the electrical network. The results of the work are structural models of the expert system knowledge base, a method of experimental design to construct the sensitivity matrix of regression polynomials of the efficiency of dispatch actions, and algorithms for the functioning of the expert automation system for heuristic control of the post-contingency state of the power system. The heuristic control system provides static stability support, power flow regulation, real-time state adjustment, display of current state parameters, and monitoring of the actions of operating personnel. The practical significance of the obtained results lies in the intelligent expert automation of post-contingency power system state management. The expert system can be integrated into the control subsystem of a general automated dispatch control system, thereby increasing management efficiency and the quality of the post-contingency state. Furthermore, the system can be used to train and verify the professional knowledge and skills of operational dispatch and technical personnel.
Information technologies in energy systems engineering and manufacturing
Information technology in energy engineering and manufacturing
A mathematical model for evaluating the controllability of information management systems in complex adaptive environments
Modern information management systems operate in complex adaptive environments characterised by high dynamics, uncertainty and continuous hybrid disturbances, which significantly complicates the maintenance of coordinated control. Under such conditions, traditional approaches focused on reliability and performance of individual components are insufficient, as they do not account for systemic interactions between subsystems and the risk of loss of overall controllability. This creates a need for the development of mathematical approaches for quantitative assessment of system controllability as a key property of the resilience of critical information infrastructure. The aim of this study is to develop a mathematical model for assessing the controllability of information management systems operating in complex adaptive environments, taking into account the interaction of functional subsystems, external and internal disturbances, and control actions that influence system dynamics. Methodology: The study employs a vector–matrix approach to dynamic modelling, in which the information management system is represented as a set of interacting functional subsystems described in the state space by a system of differential equations. The model incorporates inter-subsystem interactions, degradation processes, external disturbances and control actions, as well as the influence of cognitive factors on system behaviour. Numerical verification of the proposed model is performed using the fourth-order Runge–Kutta method within a scenario-based analysis framework. The information management system is formalised as a set of five interacting functional subsystems, including infrastructural, network-communication, application, analytical-cognitive and user levels. An integral system controllability coefficient is introduced as a scalar functional of subsystem states, interaction parameters and control inputs. It is shown that this coefficient enables quantitative identification of transitions between stable, adaptive, crisis and pre-collapse operating regimes. An interpretation scale is developed that relates the values of the coefficient to subsystem coordination and the probability of management desynchronisation. The results of numerical modelling confirm the sensitivity of the proposed indicator to destabilising influences and its ability to reflect the effectiveness of compensatory control actions. The proposed approach extends existing methods of assessing system stability and reliability by introducing a dynamic integral indicator that captures the combined influence of structural interactions, control actions and cognitive factors on system behaviour. The scientific novelty lies in the formalisation of system controllability as a quantitative functional characteristic that reflects the ability of a complex system to maintain coordinated operation under destabilising conditions. Practical value: The developed model provides a practical tool for application in real-time monitoring systems and decision support platforms for critical information infrastructure, enabling early detection of loss of controllability and supporting the design of adaptive response strategies.
Information technologies and computer systems
Information technologyand computer systems
A method for structuring functional and technical domains of aninformation system based on a project change history
Relevance: Information systems continuously evolve through commits and iterative modifications, which gradually reduce architectural transparency and blur responsibility boundaries between components. In long-lived systems, static architectural documentation often becomes outdated, while repository change history preserves objective evidence about how artifacts evolve together and how evolutionary dependencies emerge over time. Aim of the article: The paper aims to develop a method for the automatic structuring of the functional and technical domains of an information system, based on a project change history and evolutionary coupling signals extracted from repository data. Tasks. The research tasks include formalizing the change history model, defining evolutionary cohesion indicators, constructing an integrated evolutionary cohesion graph, developing a graph-based domain grouping procedure, differentiating recovered domains into functional and technical responsibility areas, and defining the output representation and validation strategy. Methods: The proposed research combines repository mining, evolutionary coupling analysis, graph-based clustering, semantic similarity analysis, temporal cohesion analysis, and dependency-aware modeling. The approach integrates co-change frequency, temporal proximity, semantic similarity, and structural dependency signals into a unified evolutionary cohesion measure represented as a weighted graph. Scientific novelty: The scientific novelty of the research reflected in the paper lies in recovering domain structures not from static architecture or documentation, but from observable evolutionary behavior recorded in repository history. The proposed method introduces an integrated evolutionary cohesion measure and an explainable differentiation mechanism that separate recovered domains into functional and technical responsibility areas based on ownership dispersion, reuse index, and cross-domain connectivity indicators. Practical significance: The proposed method improves architectural transparency and supports maintenance planning, refactoring, change impact analysis, technical debt management, and responsibility reasoning in evolving information systems. The resulting domain map and interaction graph can assist developers in identifying highly coupled areas and understanding evolutionary dependencies between subsystems. Results. The paper presents a complete pipeline for transforming repository evolution data into an interpretable domain structure model. The method constructs an evolutionary cohesion graph, identifies candidate domains through graph-based grouping, and classifies them into functional and technical areas using explainable indicators. An illustrative example demonstrates how stable co-evolution patterns form interpretable domain areas even without explicit architectural documentation. Conclusions. The proposed method enables recovering interpretable functional and technical domain structures from a project change history by combining co-change, semantic, dependency-aware, and temporal signals. The resulting domain decomposition supports architectural understanding and maintenance-oriented reasoning while reducing dependence on incomplete or outdated architectural documentation. Full empirical validation and further enhancement of classification and weighting strategies remain directions for future research.
Fuzzy Brain-State-in-a-Box neural network for intelligent classification of solar panel defects
The article considers the problem of intelligent classification of solar panel defects in remote monitoring systems based on unmanned aerial vehicles. The relevance of the study is due to the need to increase the accuracy of automated diagnostics of photovoltaic systems in conditions of incomplete data, the presence of noise components and partial overlap of features of different types of defects. The use of traditional computer vision methods and deep neural networks for unmanned aerial vehicle monitoring tasks is often accompanied by significant computational complexity, the need for a large amount of training data and the complexity of implementation on low-power edge-AI platforms. In this regard, the development of compact intelligent classification models capable of operating in conditions of limited computing resources is relevant. The work aims to develop a fuzzy neural model Fuzzy Brain-State-in-a-Box, for intelligent classification of solar panel defects in a compact feature space. The proposed approach combines the mechanisms of Brain-State-in-a-Box associative memory, fuzzy inference and prototypical class representation within a single hybrid architecture. The research methodology is based on the formation of a five-dimensional feature space after preprocessing and dimensionality reduction of experimental data. To increase the stability of the classification, a temperature-controlled fuzzy membership evaluation mechanism was used, which provides adaptive regulation of the level of fuzziness of the classification solution. As part of the study, a software implementation of the Fuzzy Brain-State-in-a-Box model was performed and experimental studies were conducted on a dataset of one thousand samples. The results confirmed the effectiveness of the proposed approach and provided a classification accuracy of ninety-one percent, a macro-F1 measure at the level of eighty-nine hundredths and Cohen's Kappa at the level of eighty-six hundredths. The analysis showed that the use of fuzzy degrees of membership and prototypical representation of classes allows for increasing the model's resistance to noise and partial overlap of features. The practical significance of the work lies in the possibility of using the developed Fuzzy Brain-State-in-a-Box model in systems for monitoring and diagnosing defects of solar panels based on unmanned aerial vehicles and edge-AI platforms, where low computational complexity, compactness of the algorithm and the ability to work in real time are important.
Theoretical aspects of computer science, programming and data analysis
Theoretical aspects of computer science, programming anddata analysis
Enhancing the accuracy and interpretability of real estate price predictions using machine learning methods
Relevance: stems from the increasing need for machine learning models in real estate that are not only accurate but also interpretable, since practical applications require a clear understanding of the factors influencing predictions, particularly in decision-making related to urban development and property valuation. Aim: the aim of the article is to develop and evaluate an approach that simultaneously improves predictive performance and enhances the interpretability of machine learning models when working with heterogeneous data sources. Objectives: the objectives include analysing multiple feature groups, namely textual property descriptions, spatial indicators such as distances to key infrastructure objects, and visual characteristics derived from satellite-based night-time illumination data, as well as assessing their combined impact on model performance. Methods: the study is based on gradient boosting over decision trees using the Light Gradient Boosting Machine algorithm, the construction of spatial features through distance-based metrics, text processing techniques, and interpretation tools based on Shapley values and partial dependence analysis to reveal feature influence. Scientific novelty: the novelty lies in integrating heterogeneous features of different origin within a unified modelling framework and combining complementary interpretation techniques to identify nonlinear relationships and interaction effects. Practical significance: the proposed approach can be applied in automated valuation systems, urban analytics, and decision-support tools, providing a more transparent understanding of price formation mechanisms. Results: the results show that the proposed modelling approach based on the Light Gradient Boosting Machine provides high predictive performance. Starting from the baseline feature set, the model achieves an RMSE of 374 and an R² of 0.818. The integration of heterogeneous feature groups further improves the model performance, reducing the RMSE to 348 and increasing the R² to 0.839. It is also evident that textual and visual features play a noticeable role, as they help capture nonlinear patterns and threshold-like effects that are difficult to detect otherwise. Conclusions: overall, the proposed approach not only increases predictive accuracy but also makes the model behavior easier to interpret, leading to a more transparent and reliable analysis of real estate prices and supporting its use in practical applications.
Long Short-Term Memory based reference model method to nonlinear dynamic system identification
The relevance of this research stems from the fact that modern engineering problems and intelligent control systems require highly efficient mathematical tools for modeling complex nonlinear dynamic systems, capable of resolving the fundamental trade-off between ensuring approximation accuracy and minimizing computational costs and model training time. Traditional approaches based on neural networks with time delays are limited by a fixed memory window size, which makes it impossible to adequately describe processes with deep delays and non-stationary modes without a significant increase in the number of parameters. The aim of this work is to reduce the time required to construct models of nonlinear dynamics while ensuring a specified modeling accuracy by developing the reference model method through the use of neural network architecture with long-term short-term memory. To achieve this goal, the following tasks are set: to carry out a theoretical development of the reference model method through a transition to dynamic recurrent structures, to develop an algorithm for parameter alignment of reference models with long short-term memory to overcome permutation invariance, and to perform experimental verification of the proposed approach. The research methods are based on system identification theory, transfer learning methodologies, and model fusion. To eliminate the permutation symmetry of hidden layers, a weight tuning optimization algorithm based on solving linear programming problems has been adapted, which allows independent parameter matrices to be transferred to a common loss function pool before their superposition. The results of the work include the formalization of a reference model method based on long short-term memory and an algorithm for parameter alignment of these models, which ensures linear connectivity of modes in parameter space. Experimental verification on a test nonlinear system demonstrated that the proposed method accelerates model convergence by nearly a factor of 2 compared to an approach based on neural networks with time delays and reduces the final error by a factor of 3.3. The obtained results confirm the effectiveness of using recurrent architectures in the model superposition process. The scientific novelty of the work lies in the development of a reference model method based on long short-term memory, which allows overcoming the limitations inherent in architectures with time delays associated with the finiteness of the memory window. This ensures adequate modeling of systems characterized by deep delay, significant inertia, and complex nonlinear hysteresis effects. Furthermore, to address the problem of permutation invariance of hidden states in independently trained recurrent networks – which traditionally precludes direct averaging of model weight parameters – this work adapts a specialized weight-tuning optimization algorithm to the structure of long short-term memory.
Context-aware educational chatbots via retrieval-augmented generation
Relevance: The rapid digital transformation of higher education has created growing demand for context-aware academic support. Existing educational chatbots suffer from poor grounding and high hallucination rates. Purpose. This study develops a context-aware educational chatbot framework that improves reliability and contextual relevance in bilingual academic environments. Tasks: The tasks include constructing a bilingual corpus, contrastively fine-tuning a multilingual retrieval model, and evaluating the framework against baselines. Methods: Course documents are converted into a structured Vietnamese–English question–answer corpus via semantic chunking, automated question–answer generation and paraphrase augmentation. A Transformer-based bi-encoder is contrastively fine-tuned and indexed in a Facebook AI Similarity Search vector database. Retrieved passages and conversation history are combined before response generation. Scientific novelty: The study integrates a data-centric pipeline with retrieval-augmented generation for bilingual education and introduces contrastive fine-tuning of a compact multilingual bi-encoder for retrieval tasks. Practical significance: The framework delivers a scalable, resource-efficient solution for intelligent tutoring with potential for institutional deployment. Results: Experimental evaluation demonstrates strong retrieval accuracy and contextual relevance. The system retrieves the most relevant content in nearly eighty-seven percent of cases and retrieves relevant information within the top results in more than ninety-five percent of evaluations. Compared with retrieval-free systems, the proposed framework improves factual consistency and reduces inaccurate responses by more than one-half while achieving stronger contextual relevance and semantic similarity than a standard baseline. Conclusions: Structured corpus construction and retrieval quality are key factors affecting chatbot performance. The proposed compact retrieval-augmented framework consistently improves factual grounding and contextual relevance in bilingual educational settings.
Improving the information compression algorithm in the unmanned aerial vehicle communication system
The problem of processing large amounts of data transmitted over wireless communication networks with low bandwidth is a pressing one, and it is one of the main obstacles to the effective deployment of such networks. The most rational way to solve this problem is to conduct research on improving the compression method, or using a combination of data compression methods, which will reduce the volume and time of data transmission. The purpose of the research is to increase the compression of data transmitted between an unmanned aerial vehicle and a control point and reduce the processor time for compressing one packet of the source coding process for a wireless communication channel using the MAVLink protocol in conditions of limited resources of the microcontroller of an unmanned aerial vehicle. The task set in the work was to develop an improved lossless data compression algorithm when using it within the Mavlink v2 protocol and evaluate the compression efficiency. The methods used were to improve the source stream compression when exchanging data using the MAVLink protocol, which is characterized by small packet volumes. Source coding with lossless compression was carried out by forming bit masks and pairwise compressing them according to their properties. The scientific novelty consists in the development of a sequential pair source coding algorithm for a wireless communication channel, which implements stream coding of bytes of the packet structure, which provides lossless information compression. The practical significance of the results obtained lies in the possibility of effective application in the development of communication systems for control and information exchange between a control point and an unmanned aerial vehicle using the MAVLink protocol. The results of the work are the improvement of the source coding algorithm for effective lossless compression of MAVLink packets in communication systems under conditions of limited resources of an unmanned aerial vehicle, and the establishment of the inefficiency of known algorithms for small MAVLink packets. that the total volume of packets is reduced by fifty-four percent. Comparison of the compression efficiency of the proposed improved source coding algorithm and known compression algorithms showed that the total volume of packets is reduced by seventeen percent. In addition, when using the proposed algorithm, the processor time consumption for compressing one packet is significantly reduced (on average, compression of one packet lasts less than a microsecond), which is significantly less than when using known compression algorithms. The conclusions of the work are the proposal of an improved compression algorithm for a wireless communication channel, which provides a higher compression ratio compared to the considered algorithms during streaming serial-pair encoding of bytes of MAVLink protocol packets.
Computer simulation of an electric bicycle wheel drive with real load emulation
Topicality. Micro-mobile electric vehicles, whose drives are based on brushless DC motors, have become widespread in the last decade. The features of the electric drive systems of these vehicles and their on-board power supply systems are the subject of scientific research aimed at solving such key problems as: increasing the driving range, reducing electromagnetic torque ripple, improving the efficiency of regenerative braking, etc. A review of the literature shows that solving each individual problem involves two different approaches: computer simulation and physical experimentation. Purpose and objectives. To combine both approaches of testing: conducting experimental research with real vehicle systems and simulating the effect of road load and real dynamic load on the drive in transient modes using a corresponding load emulator. The latter consists of hardware that generates mechanical load on the drive and software that allows simulating various specified and repeated vehicle motion modes in experiments. Methods. In this work, the Hardware-In-The-Loop approach is applied to an electric bicycle test bench, in which a real wheel with in-wheel motor is connected via a friction transmission to a DC loading machine controlled by a two-quadrant DC-DC converter. Results. In this work, to control the torque of the loading machine, an emulator control system structure was developed, in which the conditions for the movement of the electric bicycle are set and tasks related to these conditions are formed for static and dynamic loads. A computer model of a test bench for studying the electric bicycle drive, developed in the Matlab/Simulink environment, made it possible to identify the features of the hardware part of the in-wheel motor load emulator. In particular, during the emulation of a dynamic load associated with the high moment of inertia of the drive, when the electric bicycle starts and initially accelerates at low speeds, the two-quadrant DC-DC converter is unable to provide the required value of the braking current of the load machine. Therefore, it was necessary to ensure the operation of the DC-DC converter in the third quadrant for the specified mode using an additionally introduced two-transistor switch, which allows switching the operation of the DC-DC converter from the second to the third quadrant and vice versa. A control system has been developed that performs such switching. Conclusions. A comparison of the results of computer simulation of the operation of the electric bicycle in-wheel motor drive on a test bench with load emulation with the corresponding results of simulation of the operation of the same drive in an electric bicycle under the same driving conditions showed a discrepancy in the main motion coordinates of no more than of seven percent, which confirms the adequacy of the emulation and the operability of all solutions adopted for the creation of the test bench.
Method for video data transmission at the application layer with adaptive control in an unmanned aerial vehicles network
Video streams transmitted from unmanned aerial vehicles are a critical source of situational information for monitoring, search-and-rescue, emergency response, and security applications. In such time-critical scenarios, the practical value of a video frame rapidly decreases after a deadline, while unmanned aerial vehicle networks are characterized by stochastic variability of delay, jitter, losses, and dynamic topology. Therefore, the problem of ensuring timely delivery of video frames through adaptive, application-layer control becomes relevant. The object of research is the process of video frame transmission in unmanned aerial vehicle networks under stochastic channel and routing conditions. The subject of research is methods and models of adaptive control of video frame delivery at the application layer, including strategy selection under deadline constraints. The purpose of this paper is to develop an application-layer method for adaptive transmission of video frames that increases the probability of delivery before a deadline and reduces the fraction of late-or-lost frames in unmanned aerial vehicle networks. A conceptual framework for adaptive control of video frame transmission is proposed, including a decision model for selecting transmission strategies based on observed network conditions and deadline requirements. An adaptive algorithm for per-frame strategy selection is developed, incorporating rules for retransmission limitation, buffering management, and robustness mechanisms (e.g., redundancy-based delivery). A set of evaluation metrics is defined to characterize timeliness and reliability, including the probability of on-time delivery, average delay of delivered frames, loss probability, and the late-or-lost fraction. Simulation-based assessment demonstrates that adaptive control improves deadline reliability compared to fixed transmission strategies across representative unmanned aerial vehicle network regimes.
Model and method for evolutionary control of the decision-making structure in intelligent systems
The relevance of the study is обусловлена the fact that modern intelligent software systems operate under conditions of incomplete, conflicting, and dynamically changing data, as well as limited computational resources, which necessitates improving the adaptability, robustness, and correctness of decision-making processes compared to traditional approaches focused on tuning individual algorithms. Furthermore, the increasing complexity of information environments and the need to process heterogeneous data sources intensify the requirements for consistency and reliability of analysis results. Under such conditions, the transition to controlling the structure of the decision-making process as a higher-level object becomes particularly important. The aim of the study is to improve the adaptability, robustness, and correctness of decisions by shifting from the tuning of individual algorithms to the controlled modification of the structure of the analysis process, including the composition of active software components, the order of their interaction, and the rules governing transitions between processing modes. The methods of the study are based on the approach of evolutionary control of the decision-making structure, which involves controlled transformations of admissible configurations, integration of fuzzy interpretation and evidence aggregation, assessment of the reliability and consistency of information sources, as well as formal proof of correctness, finite convergence, and local optimality properties. The proposed approach enables adaptive selection of the analysis process configuration depending on the level of uncertainty and available resources. In addition, the method ensures a balance between decision-making accuracy and computational resource costs under dynamic operating conditions. The results of the study consist in the development of a model and a method for evolutionary control of the decision-making structure, as well as their practical implementation in the form of an intelligent medical diagnostic system, where experimental studies have shown that as the level of uncertainty increases, the system automatically activates additional mechanisms for source consistency control and predictive analysis, thereby providing enhanced robustness of results compared to a baseline fixed configuration.
Implicit vs explicit reasoning in strategic narrative detection
Relevance of the research: The proliferation of state-sponsored propaganda across the global digital ecosystem constitutes a severe threat to international security and democratic stability. As cognitive warfare evolves, adversaries rely on subtle, culturally nuanced strategic narratives to polarize public opinion and alter collective behavior. Defending the information space requires advanced natural language processing systems capable of deep, context-aware reasoning to identify and deconstruct manipulative narratives in real time, moving beyond traditional manual analysis. Aim and objectives: This study evaluates the efficacy of advanced artificial intelligence Reasoning Models in classifying strategic narratives. The research investigates the interplay between internal computational reasoning budgets and the enforcement of explicit step-by-step reasoning instructions, seeking to uncover how modern architectures process geopolitical nuances and identify potential interference effects caused by traditional instruction techniques. Methods used: To achieve this, the investigation conducted extensive empirical evaluations across a diverse spectrum of large language models utilizing the DIPROMATS 2024 dataset, analyzing social media posts published by diplomatic authorities. The experimental framework isolated variables by varying internal reasoning resources and prompt output instructions. The methodology bypassed complex modular architectures in favor of highly optimized, singular instruction configurations embedded with contextual examples. The assessment rigorously measured exact classification precision, operational latency, and economic efficiency. Results: The findings reveal a significant shift in optimal processing strategies. The research identified a critical reasoning paradox, demonstrating that enforcing explicit step-by-step reasoning actively degrades the classification accuracy of some highly capable models by disrupting their optimized latent analytical pathways. Furthermore, a stark architectural divergence emerged regarding computational budgets. While certain models improved with increased reasoning time, others suffered severe performance degradation when allocated maximum computational resources. Conversely, optimized lightweight models demonstrated an unparalleled combination of processing speed and financial efficiency. Conclusions: The research concludes that modern cognitive models inherently possess an advanced capacity to natively detect latent propagandistic frameworks, bypassing the need for extensive supervised training data. By exposing the interference effect, the study challenges traditional prompt engineering paradigms and provides the empirical benchmarks required to integrate scalable, real-time strategic narrative detection within comprehensive information security defense systems.
Cross-modal representation learning for accurate harmonized system code classification in е-commerce systems
Accurate classification of goods according to the Harmonized System remains a critical challenge in international trade and e-commerce due to the complexity of product descriptions, ambiguity of textual data, and variability in product representation. The novelty of this study lies in the development of a cross-modal representation learning approach for automated Harmonized System code classification that integrates both textual and visual product information within a unified framework. By leveraging multimodal data, including product descriptions and images, the proposed system improves classification accuracy and robustness compared to traditional approaches that rely solely on textual information. In addition, the proposed framework enables more reliable identification of product characteristics by aligning semantic and visual representations in a shared feature space, which enhances the model’s ability to handle incomplete or ambiguous product descriptions commonly encountered in e-commerce environments. The methodology is based on contrastive learning techniques that align semantic representations across modalities, enabling the model to capture deeper relationships between product attributes and Harmonized System codes. Transformer-based encoders are employed for textual feature extraction, while convolutional or vision transformer architectures are used for image representation. A joint embedding space is constructed to facilitate effective cross-modal interaction and classification. Experimental evaluation is conducted on a real-world e-commerce dataset, demonstrating that the proposed approach significantly outperforms baseline models in terms of accuracy, precision, and recall. The results highlight the effectiveness of multimodal learning in handling noisy, incomplete, and heterogeneous product data commonly encountered in customs and trade environments. The proposed framework contributes to the advancement of intelligent customs classification systems by enhancing automation, reducing human error, and improving compliance in international trade operations. Future work will focus on incorporating explainability mechanisms and extending the model to support multilingual and low-resource scenarios.
Controlled spectral parameter synthesis based on an adaptive informative subset
The relevance of the study is due to the increasing complexity of inverse spectroscopy problems arising in materials science, optoelectronics and related fields, as well as the limitations of classical methods of spectral synthesis of parameters, which are based on the use of the full spectrum. This leads to high computational costs, reduced numerical stability and deterioration of the identifiability of parameters in the presence of noise. In this regard, it is relevant to develop methods that provide a controlled reduction in the dimensionality of spectral data without losing the physical correctness and accuracy of parameter synthesis. The aim of the work is to develop a method of controlled spectral synthesis of parameters, in which the inverse spectral problem is solved on an adaptively formed informative subset of the spectrum. To achieve the set goal, the following tasks were solved: the concept of spectral informativeness was formalized based on the sensitivity analysis of parameters; a mechanism for adaptive formation of an informative spectral subset was developed; A controlled computational cycle of parameter synthesis using a physically based model was constructed; criteria for stability and stopping the synthesis process were determined. The work applied methods of mathematical modeling of spectral characteristics, variational optimization methods, sensitivity analysis, regularization of inverse problems, and principles of controlled computational processes. In the developed method, spectral synthesis of parameters is considered not as a one-time optimization procedure, but as a closed controlled cycle with dynamic adaptation of the spectral region of analysis. The results obtained demonstrate that the use of an adaptive informative subset of the spectrum allows to significantly reduce the amount of spectral data while maintaining an admissibly small relative error in parameter estimation. It is shown that in the process of iterative controlled synthesis, weakly informative spectral sections are automatically excluded, for which the sensitivity of the model to parameter variations is low or degenerate, which directly leads to an improvement in the conditionality of the inverse problem. The reduction of spectral redundancy and the concentration of analysis on informative areas ensure the reduction of the influence of noise disturbances on the identification results and the stabilization of the functional minimization process. As a result of numerical testing, the existence of a compromise between the accuracy of parameter synthesis and the volume of spectral information used was confirmed, which within the framework of the developed method is implemented in a controlled, reproducible and without loss of physical interpretability of the results. The method was tested on experimental spectral data of a nanosecond discharge plasma, which confirmed its suitability for the identification of Zn, N2 and N II concentrations. The practical significance of the results obtained lies in the possibility of increasing the efficiency and stability of inverse spectroscopic calculations in applied information systems.
Convolved numbers of k-section of the Fibonacci sequence: properties, consequences
Chebyshev polynomials are used to solve numerous applied problems in computer science involving interpolation theory, approximation theory, numerical analysis, dynamical systems theory, and number theory. However, when forming pseudo-random recurrent sequences, the use of derivatives of Chebyshev polynomials, especially of higher orders, is much less common in the literature, although the issue is quite actual. This article somewhat fills this gap. It is shown that among recurrent sequences used for information analysis and to improve its cryptographic protection, the Fibonacci sequence and its generalizations are the most popular. This article considers a further generalization of Fibonacci numbers, namely, folded numbers k-sections of the Fibonacci sequence. The objective of the research is to further generalize the Fibonacci numbers, namely, the collapsed numbers of the k-intersection of the Fibonacci sequence. The research used modern methods of number theory. The properties of the obtained sequences are determined, and new connections between their elements are found. A further development of Fibonacci-type sequences is proposed, based on the relationship between the derivatives of Chebyshev polynomials of the second kind and Chebyshev polynomials themselves, as well as on the relationship between the folded numbers  the cross-sections of k the Fibonacci sequence and the derivatives of Chebyshev polynomials of the second kind through Lucas numbers. A number of identities are obtained linking Fibonacci numbers and Lucas numbers. It is shown that higher-order derivatives of Chebyshev polynomials prove to be an effective basis for solving certain problems in number theory, namely, the construction of new sequences. Thus, the study resulted in a family of generalized sequences with higher order growth and more complex coupling coefficients than the known Fibonacci sequences. This makes the resulting generalized sequences ideal for sparse data compression and for solving a number of problems in information security. It is proven that the resulting sequences are original and are not presented in the OEIS encyclopedia, confirming the potential of the proposed approach to the formation of various sequences that can be used to improve the reliability of information systems.
derivatives of Chebyshev polynomials of the second kind
Integrated system of methods for automating the evaluation of risk management effectiveness in software quality assurance systems
Relevance: The paper addresses the problem of quantitative evaluation of risk management effectiveness in software quality assurance systems under the increasing complexity of software products and the need to integrate risk management activities into the Software Development Life Cycle. Traditional approaches based on expert judgment and periodic audits are shown to lack measurable outcomes and fail to establish causal relationships between risk response actions and changes in key quality metrics. Aim of the article: The purpose of the study is to propose and validate a integrated system of methods for evaluating risk management effectiveness through the integration of technical and economic indicators. Tasks: The research focuses on integrating a risk register, dynamic software quality metrics (defect density, test coverage, mean time to failure, security incident rate), and cost indicators associated with mitigation activities into a unified assessment framework. Methods: A mathematical model is developed to describe the interaction between risk exposure, management actions, and the evolution of quality metrics within a sprint-based Software Development Life Cycle. The integrated system of methods is implemented as a software prototype of a Decision Support System module that enables automated risk monitoring. Scientific novelty: The study lies in the development of an Integrated system of methods that formalizes the interaction between a risk register, a dynamic quality metrics model, and an integral effectiveness criterion. This Integrated system of methods ensures reproducibility and comparability of results by creating a unified algorithmic framework for sprint-based evaluation. Practical: significance: The proposed approach is applicable within Decision Support System environments, Continuous Integration and Continuous Deployment pipelines, and software monitoring tools for continuous quality assurance. Results: Simulation results demonstrate balanced improvements in quality metrics, a decrease in overall risk exposure, and higher integrated effectiveness values for the risk-aware strategy compared to the baseline approach. Conclusions: An Integrated Risk Management Effectiveness Index is introduced, combining normalized quality improvements, reduction of risk criticality, and economic factors into a single interpretable indicator for automated managerial analysis.
Comparative evaluation of deep neural networks for brain tumor classification from magnetic resonance imaging
The growing prevalence of brain tumors and the complexity of magnetic resonance image interpretation create a need for automated decision support tools that can improve diagnostic accuracy and reduce the workload on medical specialists. Modern deep learning approaches demonstrate significant potential in medical image analysis, but the selection of appropriate model architectures requires systematic comparative evaluation considering accuracy, robustness, and computational efficiency. The aim of this study is to evaluate the performance of modern deep learning architectures for multiclass brain tumor classification based on magnetic resonance images and to determine their suitability for practical implementation under different computational conditions. The research is based on the application of transfer learning using several convolutional neural network architectures. The dataset was prepared through cleaning, normalization, and augmentation procedures to improve model generalization and robustness. The models were trained and evaluated using multiple quality criteria, including classification performance, stability under data distortions, and interpretability analysis based on visualization of decision regions. Computational complexity and inference efficiency were also analyzed to assess deployment feasibility. The comparative evaluation demonstrated that deep neural networks are capable of reliably distinguishing between different tumor types and normal brain conditions. One architecture showed the highest overall classification performance and robustness to noisy input data, while architecture provided a more balanced trade off between computational efficiency and prediction quality, making it suitable for resource constrained environments. Visualization analysis confirmed that the models focus on diagnostically relevant regions of the images, supporting the validity of their predictions. The study confirms the effectiveness of deep learning models for automated brain tumor classification and highlights their potential for integration into intelligent medical decision support systems. The obtained results demonstrate practical value for clinical applications, educational purposes, and further research in medical image analysis, particularly in scenarios requiring accurate and fast preliminary diagnosis.
Optimizing the control of a transactive energy system with periodically changing architecture based on Internet of energy
The article proposes a solution to the problem of improving the quality and efficiency of control systems for transactive energy systems, which are characterized by repeated periodic changes to their architecture. Information technologies are expanding the capabilities of microgrids, one such technology being the Internet of Energy (IoE). IoE is a global interconnected network consisting of smart grids interacting with each other via information technologies. IoE facilitates the convergence of cyber-physical and economic indicators of network operation, providing the ability to improve control quality by predicting expected changes in dynamics. The proposed solution for IoE control optimization is based on load change forecasting technology, ensuring efficient power distribution between generators and an optimal generator on/off schedule. The optimization criterion and constraints are formulated mathematically. The proposed load forecasting technology is based on the use of the eigenvalues of the state matrices of the transactive energy system. The results of calculating the eigenvalues calculated by the methods of the first group (the Power method and the Khilenko method) and the second group (the Krylov method and others) are presented. The article considers a transactive system with a variable structure based on the Internet of Energy, which can consist of a different set of generation sources, solving an optimization problem based on technical and economic criteria. The solution of the optimization problem of minimizing the cost of primary fuel for diesel generators to reduce costs and reduce CO2 emissions using power distribution programs for forecasted load schedules and forecasted RES schedules is demonstrated using examples of two transactive systems.
Anomaly detection on time series data for autonomous robot operation
Autonomous robots deployed in safety-critical applications require real-time monitoring systems to detect both environmental and mechanical anomalies. While anomalies in visual sensor data (changing lighting, unexpected obstacles) are often obvious, mechanical failures – such as motor malfunctions or wheel slippage – may not be directly observable in images, making detection challenging. Additionally, real-world operational data is severely imbalanced: normal behavior is well-represented, but anomalous events are rare. This imbalance renders traditional supervised learning approaches ineffective. To address these challenges, this paper presents a staged multimodal autoencoder architecture – a neural network system that processes both visual information (RGB camera images) and motion sensor data simultaneously. Unlike conventional multimodal systems that train all components jointly and suffer from modality competition, proposed architecture employs a three-stage training curriculum that trains visual and motion encoders independently before joint optimization, preventing gradient imbalance and ensuring robust representations. The system performs anomaly detection through reconstruction error analysis: lower errors indicate normal operation patterns, while deviations signal potential anomalies. The method requires only normal operational data for training – no labeled anomaly samples are necessary. Experimental validation demonstrates that the architecture detects visual anomalies (color distortions, unexpected objects) and motion anomalies (sudden stops, jerks, velocity changes) in real-time. The proposed method is needed for safety-critical applications like autonomous robot navigation and warehouse automation, where detecting mechanical and environmental anomalies is essential for operational safety.
Non-numerical information processing in biotechnical systems with biofeedback
The paper considers issues of coordinating functional and cybernetic models when creating biotechnical systems with biological feedback. It is noted that the functional model of a biological object, built on the imperative principle of achieving a useful result, has a number of features, the most important of which is the ambiguity of the response to the input impact. The principle of the constancy of the output response of a technical object to the same input stimulus is fundamental for the construction and testing of hardware and software. The reasons for the emergence of uncertainty in the output feature space of a biological object as a response to an input stimulus are analyzed. The necessity of processing the output feature information of a biological object in real time by means of non-numerical transformation of a sequence of samples with the determination of the Kameni median and decision-making based on the sign criterion is substantiated. The effectiveness of using a non-numerical approach for statistical processing of data with a limited sample size and the presence of anomalous outliers is demonstrated. As an application, the developed method and an infrared peloidotherapy chamber with radiation intensity control based on the reaction of a person in a biological feedback loop are used. The output feature space of a biological object is analyzed, and the feasibility of using skin resistance indicators is justified and modeled. The results of experimental studies of the relationship between skin resistance and heart rate indicators and the intensity of infrared radiation are presented. The possibility of controlling the permeability of human skin using a technical component of the system is demonstrated.
A modified image encryption algorithm based on the chaotic Tent map
In a number of professional image exchange industries over open communication channels, ensuring the confidentiality and security of images is a key requirement. Furthermore, image exchange must be carried out quickly. These industries include telemedicine, forensic and forensic examinations, high-resolution satellite imagery transmission, and the Internet of Things. In this context, developing image encryption algorithms that meet these requirements is highly relevant. Encryption systems based on chaotic maps offer promising solutions for such problems. Chaotic map-based encryption is a data security algorithm (most often, images) that exploits the properties of deterministic chaos to generate pseudorandom sequences. The key advantages of these algorithms are sensitivity to initial conditions; speed—the algorithms are mathematically simple and faster than many classical ciphers; and security—they provide protection against statistical attacks and brute-force attacks thanks to a huge key space. These algorithms are used to encrypt multimedia (particularly images) in industries where high-speed processing of large volumes of data while maintaining a high level of confidentiality is essential. A modified image encryption algorithm based on the Tent chaotic map has been developed. This algorithm generates a long pseudorandom sequence based on a short key (seed). This sequence is then applied to image encryption using the Vernam algorithm. The sequence can be long enough to encrypt a large image. An integrated encryption quality criterion has been developed for comparative evaluation of encryption quality. The integral criterion combines encryption quality metrics (correlation, entropy, and resistance metrics) into a convolution constructed using the ideal point method. To evaluate the quality of the developed encryption algorithm, a preliminary computer experiment was conducted. In the experiment, the developed algorithm was compared with well-known stream and block encryption standards. The experiment revealed that, by most individual criteria and by the integral quality criterion, the developed algorithm is virtually equal to the standards, but operates significantly faster. This allows us to recommend the developed algorithm for real-time image encryption. A limitation of the developed algorithm is its inability to work with compressed images.
Methodology for land cover change detection in aerial images using a deep convolutional neural network and gradient boosting
Tracking surface land cover changes using high-resolution aerial imagery is complicated by the varying spatial resolutions of images, spectral variability, and the presence of optically comparable anthropogenic features. Identifying amber extraction sites is especially difficult because of their fragmented morphology, numerous localized impacts of diverse geometries, and substantial internal heterogeneity. Such factors drastically reduce the precision of traditional pixel-based or threshold-driven methods. The work aims to develop a methodology for automated detection of land cover changes using high-resolution aerial photographs, utilizing a deep convolutional neural network to extract high-level spatial texture features and gradient-boosting algorithms to subsequently categorize the observed changes. To achieve this objective, a multi-stage processing pipeline is implemented, including aerial image preprocessing and manual annotation, formation of training and testing datasets, patch-level representation of high-resolution scenes, and spatial aggregation of classification results into continuous thematic maps. The methodology explicitly accounts for the spatial context and textural organization of the surface, enabling reliable discrimination between amber mining areas and other visually similar anthropogenic changes. It was implemented in Python, leveraging TensorFlow and Keras for deep learning, along with a gradient boosting framework for final classification. The outputs include a thematic map with three semantic classes: “no change,” “amber mining zones,” and “other changes”, and vectorized contours of disturbed areas, facilitating a spatially explicit representation of changes. Quantitative evaluation using the harmonic mean of precision and recall, the intersection over union coefficient, the root mean square error, the mean absolute error, and the zero-mean normalized cross-correlation demonstrated higher performance for amber mining detection, outperforming standard neural network models. The practical utility of this work is its potential to autonomously track human-induced environmental impacts, aid ecological restoration and resource management, and inform management decisions in natural resource governance. The approach offers a scalable solution for high-resolution aerial imagery analysis, advancing intelligent remote sensing technologies and precision environmental monitoring.
A method for detecting financial phishing in instant messengers using an ensemble of dialogical intelligent assistants based on large language models
In the context of the rapid digitalization of financial services, instant messengers have become the dominant communication channel, which has led to an increase in the activity of cybercriminals in this segment. Financial phishing in instant messengers takes the form of complex sociotechnical attacks, the recognition of which using traditional signature methods and even classical neural network tools is complicated, since such attacks are based on psychological manipulations and contextual mimicry, which requires the use of large language models for deep semantic analysis of content. At the same time, the practical application of individual large language models is limited by their tendency to generate false facts and uneven sensitivity to different threat vectors, which makes the use of ensemble approaches relevant, which potentially provide increased recognition efficiency. The aim of the work is to increase the efficiency of detecting financial phishing in instant messengers by developing and experimentally testing a method for detecting financial phishing in instant messengers using an ensemble of dialogical intelligent assistants based on large language models. The original feature of the developed method is the use of an approach to the aggregation of recognition results, which is based on the mechanism of weighted linear convolution of responses of the ensemble of dialogical intelligent assistants taking into account the adaptive coefficients of their competence. To ensure the adaptability of the method and determine the weight coefficients of the competence of the models, an automated calibration procedure was developed using an iterative cross-validation algorithm. Also, within the framework of the proposed method, a classification of financial phishing features was carried out, which allowed identifying six dominant attack vectors, in particular: imitation of official institutions, creation of artificial urgency, technical masking of links, incitement to compromise confidential data, requests for anomalous transactions and linguistic deviations. For each of the indicated vectors, recognition criteria were formed, implemented in the target predicates of the queries. A formalized query structure has been developed, which includes components of role initialization, contextualization and criterion evaluation, which allows to unify the process of interaction with dialogical intelligent assistants and ensure stable results. Experimental studies conducted on a control sample involving the ChatGPT, Gemini and DeepSeek models have shown the high efficiency of the developed approach. The overall classification accuracy when using the proposed method exceeds the results of individual large language models. At the same time, the probability of missing phishing messages has been reduced by half while maintaining a low level of false positives.
Hybrid neural network-heuristic model for forecasting the energy demand of livestock farms
Relevance. The article is devoted to the development and investigation of neural network and hybrid neural network–heuristic models for forecasting the energy demand of livestock farms. Purpose and objectives. The study aims to develop a hybrid neural network–heuristic model for forecasting the energy demand of livestock farms based on the integration of recurrent neural networks and heuristic optimization algorithms, enabling improved short-term forecasting accuracy under variable production and external conditions. The study develops a neural network model for forecasting the energy demand of a livestock farm based on recurrent architectures of long short-term memory and gated recurrent units, capable of processing multifactor hourly time series that characterize the operating modes of farm equipment. Methods. The baseline forecasting model is based on recurrent neural network architectures, including long short-term memory and gated recurrent units, while hyperparameter adjustment is performed using an evolutionary genetic algorithm. Model implementation and computational experiments were carried out using the Python programming language in a cloud computing environment. Results. The baseline neural network model demonstrated an acceptable level of forecasting accuracy, confirming its ability to reproduce the overall dynamics of energy consumption and seasonal variations. A hybrid neural network–heuristic model for forecasting the energy demand of livestock farms is proposed and formalized, combining a recurrent neural network forecasting module with a heuristic hyperparameter optimization algorithm. The use of an evolutionary genetic algorithm for automated tuning of the neural network architecture and training parameters is substantiated. This approach increased the model’s adaptability to changes in livestock farm operating regimes and reduced forecasting errors without a significant increase in computational complexity. As a result of the optimization procedure, an optimal neural network configuration was identified, characterized by an extended memory window, short-term forecasting horizon, and rational structure of hidden layers. The optimized model provided a noticeable reduction in forecasting errors compared to the baseline solution. To validate the effectiveness of the proposed hybrid model, computer experiments were conducted using data from a livestock farm. Conclusions. The obtained results confirmed stable forecasting performance both under average energy consumption regimes and peak load conditions, indicating the feasibility of applying the developed hybrid model as an analytical basis for decision support in the management of autonomous energy systems of livestock farms.
IMECA method of risk-based assessment and ensuring cybersecurity of Large Language Models
Large Language Models (LLMs) help perform complex tasks that previously relied only on humans. Many different areas of human activity already use this technology or are actively exploring its capabilities with a view to future integration into work processes. In addition to the positive effects of their use, there are problems of uncertain and unexpected behavior, in particular, the generation of forbidden content. Given the expanding use of these models and their behavior, it is necessary to assess the level of security and further ensure the cybersecurity of this technology. The object of the study is the processes of ensuring cybersecurity for large language models. The article proposes a methodology for countering this threat by assessing the risks of such behavior and ensuring an acceptable level of cybersecurity for LLMs using the IMECA (Intrusion Modes Effects Criticality Analysis) technique of risk-based assessment. A set of countermeasures has been developed to increase the security level of LLMs, and procedures for their selection based on the criteria of maximum productivity and best rating using a countermeasure rating matrix are defined. An example of testing and ensuring the cybersecurity of a test language model is provided, the results of which show that the criticality level of cyber risks before the use of countermeasures is significantly decreased after using the most productive and highest-rated countermeasures, but threats with a high level of cyber risk criticality still exist. Directions for future research are proposed to deepen the procedure for evaluating and ensuring the security of LLMs, given the continuous development of these models and their protection mechanisms. The main result of this work is the combination of a model for ensuring the cybersecurity of LLMs and an improved method for analyzing the criticality of their vulnerabilities for further adaptation of the IMECA method of quantitative risk-based assessment and cybersecurity assurance for the field of LLMs.
Intrusion Modes Effects Criticality Analysis
A method for constructing non-basic GL-models by combining models with arbitrary graph structures
The paper proposes a method for constructing GL-models of failure behavior for a special class of non-basic fault-tolerant multiprocessor systems. The considered class includes systems whose failure behavior is determined by the fulfillment of one of several conditions that depend on combinations of processor states and are specified by the corresponding Boolean expressions. When any of these conditions is satisfied, the system failure behavior can be described by an individual auxiliary model constructed using known methods. The objective of this study is to develop a generalized method for constructing models of such non-basic systems by combining multiple auxiliary models of arbitrary types into a single model that correctly represents the system failure behavior. The proposed method is based on modifying the edge functions of the auxiliary models using the Boolean expressions of the corresponding conditions, after which the modified models are combined by merging arbitrarily selected vertices of their graphs. It is shown that the resulting model reproduces the behavior of the corresponding auxiliary model under each condition and thus correctly describes the failure behavior of the original system. The scientific novelty of this work lies in the proposed approach to combining auxiliary models of a general form, which imposes no constraints on the structure of their graphs and, unlike existing approaches, does not require these models to be based on cycle graphs. This makes it possible to construct models for a wide class of non-basic fault-tolerant multiprocessor systems with complex operability conditions. A comparative analysis of the complexity of edge-function expressions in models constructed using the proposed method and those obtained by existing approaches is carried out on a set of representative examples, revealing a significant reduction in the complexity of such expressions. The case of parallel evaluation of edge-function values is considered separately, and a reduction in the maximum expression complexity is demonstrated, which is important for decreasing computation time. The practical significance of the work lies in the applicability of the proposed method to constructing models of complex non-basic fault-tolerant multiprocessor systems, as well as in reducing the complexity of analyzing their failure behavior. The presented examples and experimental results confirm the correctness of the constructed models and demonstrate the possibility of further simplification by eliminating edges with identically unit edge functions. The proposed approach can be used in the analysis and design of critical control systems, as well as in automated reliability assessment of non-basic fault-tolerant multiprocessor systems with a large number of components.