Herald of Advanced Information Technology
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.
Information technologies in energy systems engineering and manufacturing
Information technology in energy engineering and manufacturing
Copyright (c) 2026 Herald of Advanced Information Technology
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.
Information technologies and computer systems
Information technologyand computer systems
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.
Theoretical aspects of computer science, programming and data analysis
Theoretical aspects of computer science, programming anddata analysis
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
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.
Information technologies in socio-economic, organisational and technical systems
Information technology in socio-economic,organisational and technical systems
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.
Game-theoretic method of decentralized load balancing in microservice architectures
The paper presents a game-theoretic method of decentralized load balancing in microservice architectures, aimed at improving the efficiency of request distribution among service instances without using centralized controllers. The main idea is to represent the balancing process as a non-cooperative potential game in which each microservice is considered an autonomous agent seeking to minimize its own cost function. Unlike traditional algorithms such as Round Robin and Least Connections, the proposed approach is based on adaptive adjustment of agent strategies depending on the current state of the system, which ensures the achievement of Nash equilibrium and a stable load distribution. The mathematical model of game-theoretic method of decentralized load balancing takes into account the intensity of the request flow, the throughput of each node, and the quadratic component of the cost associated with resource overload. To optimize the decision-making process, a stochastic Softmax-update dynamics are used, which approximate the gradient descent of the potential function. This allows the system to gradually balance the load even in the presence of asynchronous updates and communication delays between nodes. It has been proven that the process converges to a stationary state in polynomial time, ensuring scalability and predictable behavior in large distributed environments. An experimental study conducted in the SimPy simulation environment demonstrated that the proposed method significantly outperforms classical algorithms in key metrics. Under peak load, the Game-Theoretic Load Balancer algorithm reduced the average system response time compared to the Round Robin and Least Connections algorithms. The standard deviation of processor utilization decreased, indicating a more balanced workload distribution and the absence of overloaded computing nodes. The obtained results confirm the analytical stability, convergence, and practical effectiveness of the game-theoretic approach. The developed method ensures adaptive self-regulation of the system, minimizes the risk of overload, and increases the reliability of microservice architectures. Future research should focus on integrating game-theoretic method of decentralized load balancing with cloud orchestrators and extending the model to multi-level games in hybrid computing environments.
Copyright (c) 2025 Herald of Advanced Information Technology
Unsupervised Re-identification architecture based on segmented tracklets for animal behavior analysis
In this paper, we present Mask-TAUDL, an advanced unsupervised re-identification architecture that combines instance segmentation, unsupervised deep learning, and tracklet association for detailed analysis of object behavior in long-term recordings. It combines the Mask R-CNN dual-stream detector/segmenter with dual ResNet-18 backbones and the unsupervised deep learning module based on tracklet association (TAUDL). Mask R-CNN provides accurate object localization and binary masks from which we construct tracklets with improved segmentation. The two ResNet-18 streams use these masks to extract appearance and motion-sensitive features at the tracklet level, which are combined into a common feature descriptor. The TAUDL module operates directly on the masked tracklet features and co-trains discriminative embeddings and cross-session associations without manual labeling. The proposed Mask-TAUDL architecture trains a model so that features of a single individual remain close in embedding space over time, while providing a clear separation of features between different individuals. Integrating pure masked regions with temporally aggregated features helps suppress spurious variations caused by shadows, reflections, or overlapping objects. Long-term animal re-identification is challenging due to frequent overlaps, appearance drift, and subtle visual differences between individuals, and most existing solutions rely on large annotated datasets, which limits their applicability in real-world laboratory settings. The Mask-TAUDL architecture overcomes these limitations by explicitly modeling temporally consistent, mask-refined tracks and training embeddings that preserve identity in a fully unsupervised manner. Mask-TAUDL is designed for animal behavior studies, namely small laboratory species such as mice and fish observed in closed or semi-structured arenas, where reliable long-term identity tracking is essential for quantitative behavioral analysis, longitudinal experiments, and high-throughput screening.
Comparative analysis of signal processing techniques for multimode polymer fiber interferometers
This study explores speckle pattern matching methods for deformation sensing using multimode polymer fiber interferometers. The research highlights the benefits of multimode polymer optical fibers, including their flexibility, cost-efficiency, and compatibility with complex structures, making them ideal for strain sensing applications. The primary focus is to evaluate and compare advanced image matching techniques, specifically the sum of squared differences, zero mean normalized cross-correlation, and phase-only correlation. These methods are assessed based on essential metrics such as linearity, precision, and signal-to-noise ratio. Experimental results indicate that phase-only correlation outperforms the other techniques in overall performance, offering combination of high linearity, moderate precision, and strong noise rejection. The normalized cross-correlation also demonstrates competitive results, while the sum of squared differences, despite its strengths in linearity, shows lower precision and weaker resistance to noise. The findings provide a practical framework for selecting suitable signal processing techniques to optimize multimode polymer fiber interferometers for various sensing demands. This work offers novel contributions to the field, highlighting the unique strengths and limitations of each method. These insights are expected to guide the future design and development of advanced fiber-based sensing systems.
Estimation of task-specific manipulability scores for a robotic manipulator in vacuuming scenarios
Reliable vacuum-cleaning trajectories require selecting joint configurations from which a robotic arm can continue motion in task-relevant directions without encountering self-collision, joint-limit violations, or singularities. Classical manipulability measures describe dexterity using the Jacobian but do not account for the geometric constraints and directional motion patterns typical in floor-cleaning tasks. This work presents a data-driven method for estimating a task-specific manipulability score that reflects how easily a seven degrees-of-freedom robotic arm equipped with a floor-contact vacuum tool can continue motion in six primitive directions (forward, backward, left, right, clockwise rotation, counterclockwise rotation). A dataset of one hundred and seventy-six thousand and twenty valid joint configurations was generated by sweeping a four-dimensional grid of feasible end-effector poses, computing up to three inverse-kinematics solutions per pose, and simulating incremental movements with collision checking. Each configuration was assigned a directional score based on inverse-kinematics reachability, collision outcomes, and distance-dependent penalties. A fully connected neural network was trained to regress six scores from the seven joint angles. The model achieved a denormalized Mean Absolute Error of approximately two point zero for translational directions and one point fifty-five to one point sixty for rotational directions (approximately eight percent of the full score range), while enabling extremely fast inference–around ninety-five thousand eight hundred and eighty evaluations per second on a consumer GPU. By shifting the computationally expensive stage of collision checking and inverse-kinematics sampling to offline preprocessing, the method provides a lightweight surrogate for online motion planning. The learned score can help planners avoid unfavorable configurations and maintain consistent vacuuming trajectories. Limitations include the absence of environment-aware terms, stochastic inverse-kinematics sampling, and hand-tuned scoring parameters. Future work will focus on integrating obstacle information, improving label generation, and embedding the score into trajectory optimization frameworks for more robust real-world operation.
Development of a product pricing algorithm using RFM strategy for user cohorts using machine learning methods
This paper presents a comprehensive approach to developing a dynamic product pricing algorithm that integrates RFM (Recency, Frequency, Monetary) customer segmentation strategy with machine learning methods. The research addresses the critical challenge of personalizing pricing strategies for different user cohorts in competitive retail environments, particularly for supermarkets and online marketplaces. The study extends the classical RFM model by incorporating price elasticity of demand coefficients to create a cohort-based pricing framework. Using the K-Nearest Neighbors (KNN) algorithm, the authors developed an automated classification system that accurately segments customers based on their behavioral characteristics, achieving 100% classification accuracy for repeat purchasers. The methodology comprises five key stages: data collection and preprocessing, RFM score calculation using a quartile-based ranking, customer segmentation into five distinct groups (Champions, Loyal Users, Potential Loyalists, At-Risk, and Lost), training a machine learning model for segment prediction, and cohort formation based on the timing of the first purchase. The research implements a pricing algorithm that selectively applies discounts to target segments (“Potential Loyalists” and “At Risk” customers) who demonstrate inelastic demand and have maintained activity for at least six months. Experimental results demonstrate that strategic discount application not only reduces customer churn but also increases overall revenue through enhanced purchase volume. The proposed framework, implemented in the R programming language within the RStudio environment, provides businesses with a data-driven decision support tool for optimizing personalized pricing strategies while maintaining profitability. This approach enables companies to balance customer retention efforts with revenue maximization by utilizing sophisticated behavioral analytics and predictive modeling.
Comparative analysis of chaotic and deterministic methods to territory coverage by drone swarms
This study proposes a hybrid chaotic-deterministic method for controlling a swarm of unmanned aerial vehicles, integrating social interactions such as separation, cohesion, alignment, and binding with an attraction-driven search strategy. The two-level anti-stagnation system operates at the level of individual agents (‘micro’) and the collective swarm (‘macro’) to prevent local minima and maintain controlled trajectory smoothing. The method uses double course conditioning to balance smooth trajectories with stochastic exploration. Meanwhile, an attractiveness function evaluates potential movement directions quantitatively based on territory novelty, distance factors and course stability. Social interaction forces – repulsion, cohesion, alignment and centroid binding – ensure swarm stability and collision avoidance throughout mission execution. Comparative experimental validation was conducted through multiple simulation launches for each method in irregular polygonal territories. Both the chaotic and deterministic waypoint-based approaches demonstrated exceptional mission reliability, achieving a target coverage threshold in all trials, thereby confirming complete success rates. In terms of coverage efficiency, the chaotic method achieved superior average territory completeness compared to the deterministic approach, representing a measurable improvement. However, this enhanced coverage precision comes at a significant computational time cost: the chaotic method required substantially longer average mission duration compared to the optimized waypoint method. The chaotic approach also exhibited notably higher variability in results, reflecting the inherently stochastic nature of exploration-based methods. Thus, while the chaotic method demonstrates superior coverage efficiency, it exhibits inferior time efficiency compared to the deterministic baseline. These findings quantify the fundamental trade-off between thorough exploration and time efficiency in unmanned aerial vehicle swarm operations, providing empirical evidence to inform mission-critical deployment decisions. The results suggest that chaotic methods are best suited to scenarios that prioritize comprehensive coverage and adaptability, such as search and rescue operations where undetected casualties would be a critical failure, while deterministic approaches are more effective in time-sensitive missions with predictable environments. The reliability of both methodologies, combined with the quantification of performance differences, enables the selection of methods based on evidence, aligned with specific operational requirements, mission constraints and acceptable risk-time-accuracy trade-off parameters.
Road traffic accident classification using a sparse videotransformer and adaptive fragmentation
In this work, we propose a simple yet effective approach for classifying short road traffic video clips into car accident and normal scenes. From each clip 8 frames are uniformly sampled across the sequence to ensure that key events are preserved even in longer videos. Based on the Farnebäck optical flow map, an adaptive fragment selection is performed, where the patch size (eight/sixteen/thirty-two pixels) is determined for each region of the base grid. Smaller patches are used in areas with intensive motion (to capture finer details), while larger patches are used in static regions (to reduce computations). The selected fragments are non-overlapping, resized to a uniform scale, and converted into feature vectors. The architecture operates in two stages. First, a spatial transformer processes each frame independently; attending only to the selected fragments this drastically reduces the number of feature tokens. Second, a temporal transformer processes the sequence of classify tokens (compact per-frame representations), aggregating temporal dynamics across frames. This space-to-time factorization significantly lowers computational cost and memory consumption while maintaining high informativeness in motion-intensive regions. To address class imbalance, we employ a weighted cross-entropy loss (or focal loss emphasizing hard examples) and weighted random sampling during training. Optical flow maps and fragment lists are precomputed and cached on disk, which accelerates training epochs even on CPUs without specialized hardware. Evaluation was conducted on the Car Crash Dataset (one thousand and five hundred accident and three thousand normal videos) using an eighty to twenty percent train-test split with preserved class proportions. The proposed method achieved Accuracy = 0.864 and Macro-F1 = 0.851. Preliminary comparisons show that our approach outperforms both the baseline uniform-patch Vision Transformers and traditional temporal aggregation schemes. The key advantage of the method lies in combining motion-guided feature reduction with a two-stage spatial-temporal processing pipeline, making the model suitable for realistic computational constraints (CPU-level inference) while maintaining high sensitivity to short and localized accident events. The approach is easily scalable and can be integrated with self-supervised pertaining techniques (e.g., masked video reconstruction). All experimental conditions, hyperparameters, and configurations are documented to ensure full reproducibility.
Development of a geoinformation method for assessing the accessibility of medical services in community infrastructure development projects
The article develops a geoinformation method that provides a solution to the scientific and applied problem of assessing the accessibility of medical services in community infrastructure development projects. Solving this problem is the basis for planning community infrastructure development during their post-war reconstruction. The relevance of the study is determined by the need to improve the effectiveness of management decisions regarding the assessment of medical service accessibility to community populations in conditions of spatial unevenness in the distribution of medical infrastructure facilities and limited resources. The work aims to develop a geoinformation method for assessing the accessibility of medical services for the population of communities, based on the use of open geospatial data from the OpenStreetMap framework, which takes into account the topology of the road network, the spatial distribution of medical facilities, the density and distribution of the population in the communities, and the time parameters of access to medical services. The proposed method is implemented on the basis of open data using OpenStreetMap tools, OSMnx and NetworkX libraries, which ensure flexibility, scalability and transparency of calculations. A distinctive feature of the method is the automated determination of areas of community access to medical services based on the construction of isochrones around medical infrastructure facilities, taking into account the type of facility and the acceptable time of arrival of community residents to them. The developed geoinformation method was tested for adequacy. It was found that deviations are within 1 minute or 0.3 km. The results of the experimental application of the method on the example of a typical territorial community showed its ability to identify ‘grey areas’, i.e. settlements that are not sufficiently covered by the network of medical institutions, and to assess the impact of changes in the structure of the medical network on the overall accessibility of medical services to the population. The practical value of the study lies in the creation of an algorithm that forms the basis for the development of a system to support management decisions on the strategic development of communities. In particular, in terms of assessing the population's access to medical services for planning community infrastructure development projects. The proposed method can be further expanded by integrating demographic forecasts, population mobility scenarios, and data on the state of road infrastructure, which opens up prospects for its use in a wider range of community infrastructure development project planning tasks.
Analysis of methods for supporting personalization in IT education
The increasing complexity of digital learning environments and the growing demand for individualized educational experiences underscore the importance of developing effective methods for personalizing practical tasks. The purpose of this study is to analyze existing approaches to the personalization of practical tasks in IT education and to identify their advantages, limitations, and potential for integration into digital learning ecosystems. The main objectives include the identification of technological solutions that enable automated generation and assessment of personalized practical tasks, and their effectiveness for supporting adaptive and scalable learning processes. The research methodology is based on a comparative analysis of scholarly sources, existing educational platforms, and practical implementations of personalized tasks. Special attention is given to the development of conceptual frameworks that enable personalization not only at the level of task complexity but also in terms of content variability, learning pathways, and assessment mechanisms. The review identifies the most effective strategies for achieving scalable personalization in digital education. The analysis also reveals the limitations of current solutions. The critical limitations are: insufficient support for comprehensive practical task parametrization, high technical barriers for instructors, incomplete learning management system integration, security vulnerabilities in local deployment scenarios, and limited automation in assessment processes. These constraints highlight the need for more sophisticated, user-friendly solutions. The findings provide a foundation for developing integrated information technologies that combine personalization methods with adaptive learning and artificial intelligence tools, supporting the evolution of digital learning ecosystems toward more effective, scalable, and integrity-focused educational approaches. The scientific novelty of the research lies in the systematic comparison of personalization methods and the identification of criteria for their effectiveness in IT education. The practical significance is determined by the possibility of integrating the proposed approaches into digital platforms, thereby improving the quality of training, optimizing the learning process, and ensuring the flexibility required by modern educational ecosystems.
Information model of remote sensing of soil
This paper presents a comprehensive information model and open architecture for unmanned aerial vehicle (UAV)-based remote sensing of soil. The development of remote sensing technologies using unmanned aerial vehicles (UAVs) and sensors of various measurement methods makes it possible to contactless detect various anomalies. The reliability of information after its processing is achieved by a combination of measurement methods, the correct choice of contactless measurement tools, and the use of machine learning methods for data processing and training on known anomalies. The modular UAV platform supports adaptive sensor integration and real-time data fusion for precision agriculture, environmental monitoring, and post-conflict applications. The information model defines the structure and flow of data between UAV sensors, onboard processors, and the ground control station, incorporating a digital twin for dynamic environmental modeling. A minimum set of necessary tools has been selected, based on the measurements and processing of which there will be enough information to identify anomalies in the soil with a high degree of reliability. The scope of application of the obtained results is quite extensive: from precision farming, including monitoring crop diseases and chemical non-contact soil analysis, to identifying foreign inclusions and anomalies in areas where military actions took place.
A model for keyword spotting in voice signal for specialized computer systems
Keyword spotting in voice signal is a crucial task for specialized, low-resource computer systems, such as ground drones, particularly when operating under challenging conditions with limited computational power and without reliable cloud access. This paper presents a novel, modular model for efficient keyword spotting that does not rely on deep neural networks. The model's core principle is the differential weighting of Mel-Frequency Cepstral Coefficients , prioritizing those coefficients most discriminative for phonetic content. The architecture incorporates robust signal conditioning, dynamic feature extraction (including delta and delta-delta derivatives), the transformation of acoustic features into compact string-based "fingerprints", and final classification using the Levenshtein distance. Experimental validation, conducted on a Ukrainian-language corpus of drone commands with lexicons of up to 200 words, demonstrated the model's high performance and scalability. The system achieved an F1-score of 0.92 under ideal conditions and showed significant resilience in noisy environments, maintaining an F1-score of 0.78 at a 5dB signal-to-noise ratio. Furthermore, the proposed system significantly outperformed a baseline version (using only basic Mel-Frequency Cepstral Coefficients without derivatives or normalization) by up to 33 percentage points in F1-score under challenging conditions. The study validates that this optimized classical Keyword Spotting approach provides an effective and fully autonomous solution for edge computing applications where resource efficiency and independence from cloud infrastructure are paramount, especially in critical scenarios like military operations.
Development and optimization of distributed high-performance systems with real-time data consistency
This article examines the theoretical and applied aspects of building distributed high-performance systems capable of processing large volumes of data with minimal latency while maintaining real-time consistency guarantees. The relevance of the research is determined by the rapid growth of data-intensive domains such as high-frequency trading, telemedicine, smart cities, the Internet of Things, and autonomous transport, where even millisecond delays can have critical consequences. The study focuses on the analysis of modern architectural models, including microservices, CQRS, Lambda and Kappa architectures, and event-driven systems with Apache Kafka. Methods of performance optimization are explored in detail, covering asynchronous request handling, caching, replication, and load balancing, with special attention to edge computing and 5G-based infrastructures. Particular emphasis is placed on theoretical and practical aspects of data consistency, including CAP-theorem (Consistency Availability Partition tolerance) trade-offs, consensus algorithms (Paxos, Raft), and CRDT (Conflict-free Replicated Data Type) structures. The results demonstrate that no universal architectural solution exists, but hybrid approaches combining strong and eventual consistency models can ensure reliability in mission-critical domains. The proposed analytical model for evaluating performance under different workload profiles enables the selection of optimal system configurations, balancing throughput, latency, and consistency requirements. The scientific novelty of the research lies in the integrated framework that unites architectural patterns, optimization techniques, and consensus mechanisms, as well as in the development of an analytical evaluation model that had not been sufficiently presented in previous studies. The practical significance is manifested in the formulation of recommendations for the design of fault-tolerant, scalable infrastructures for finance, telecommunications, healthcare, IoT, and smart cities.
AI-Generated video evaluation by fragment processing
Recent advances in generative AI have led to the development of techniques to generate visually realistic synthetic video. As a result, there is a growing demand for detectors capable of distinguishing between AI-generated videos. In this paper we propose a compact, fragment-based representation of video frames that enables robust spatial-temporal analysis and new approach to discrimination between real and synthetically generated footage. To achieve this goal, each frame is divided into fragments and a square matrix of size BxB is formed. Next, we compute the dominant singular value for every fragment, yielding a square S-map. This construction preserves local structure while normalizing geometry, so standard 2-D operators can be applied uniformly. We analyze spatial organization via 2-D Discrete Cosine Transform (DCT) energies and temporal change with robust thresholding to form a binary change mask. Then we apply Connected Component Labeling (CCL) on the binary change mask (4- or 8-connectivity) and compute the area of the Largest Connected Component (LCC). We derive an LCC time series that measures the spatial concentration of change. Empirically, synthetic videos exhibit higher rates of near-binary LCC toggling, longer plateaus, increased mass at rational steps, and fewer unique levels than real videos - signatures consistent with temporal quantization and procedural dynamics. The pipeline is lightweight (fragment-wise rank-1 SVD + CCL on a small grid), auditably interpretable, and suitable for batch screening and edge devices. It attains ROC-AUC ≈ 0.86 and TNR ≈ 0.94 on mixed-resolution datasets with further gains from per-granularity calibration.