Computational nanotechnology

The task of safe traffic light management is to normalize traffic. Secure management involves the implementation of the protection of information involved in the management process. The traffic light management process is a complex dynamic process. The control object is a set of traffic lights. The subject of management is a specific management system, which can be autonomous or part of a top-level management system. The implementation of a traffic light based on a neuromorphic computing base allows you to perform some of the control processes in automatic mode. As part of the control process, you have to deal with different types of data (sensor readings, control signals of different levels, etc.). Big data is processed throughout the entire control process, and one of the main tasks is to reduce the dimensionality of the information being processed. The successful solution of this problem directly depends on the model of organization of a set of traffic lights. The article discusses various traffic lights, each of which is equipped with an intelligent controller. The neuromorphic basis of the intelligent controller allows you to expand the capabilities of the computing base at a low level. Fuzzy graphs are used to represent a set of traffic lights and to connect the traffic lights to the control system. This model makes it possible to combine the information and control components of the process of interaction between the object and the subject of management into one whole. The advantages of this representation are the minimization of information necessary for the successful solution of the problem of safe management, and the expansion of the possibilities of the management process through the use of fuzzy information.

Today, one of the means of protecting network infrastructure from cyberattacks is intrusion detection systems. Digitalization requires the use of tools that can cope not only with known types of attacks, but also with previously undescribed ones. Machine learning can be used to protect against such threats. The paper presents models and algorithms for protecting against evasion attacks on machine learning components of intrusion detection systems. The novelty is that for the first time, a simulation of the use of a protection subsystem based on long-short-term memory autoencoders during a fast gradient sign attack was carried out. The methodology consists in simulating adversarial attacks with an assessment of the effectiveness of protection using classical metrics: accuracy, recall, F-measure. The results of the study showed the effectiveness of the proposed subsystem for protecting machine learning components of intrusion detection systems from evasion attacks. The detection indicators were restored almost to their original values.

Over the last few decades, both the volume of digital data in the globe and the variety of ways to use it have increased dramatically. For a long time, the Hadoop ecosystem, which is still widely utilized, has been synonymous with large data storage and processing platforms. However, during the past 20 years, Hadoop has been found to have a number of serious flaws, including the “small files problem” and uneven cluster resource usage. Various commercial and research organizations are faced with the issue of upgrading the data stack to improve resource utilization and increasing data processing efficiency. This study aims to examine the benefits and drawbacks of the next-generation data storage system, Apache Ozone, and to assess whether this technology is ready to completely supplant the Hadoop Distributed File System (HDFS).

A fundamental problem faced by modern recommendation systems is the cold-start phenomenon, which is the inability to generate personalized recommendations when historical data on user preferences is scarce. Traditional methods of solving this problem involve collecting information through questionnaires or involving data from third-party sources, which may lead to compromising user privacy. In this paper, we propose an algorithm based on Hofstede’s cultural measurement theory to generate recommendations without the need to obtain personal data directly. The algorithm establishes links between users by analyzing their cultural characteristics, which helps to improve the accuracy of preference prediction. To further improve the results, a matrix factorization method is applied to identify hidden patterns in user preferences even in the absence of explicit system interaction data. The effectiveness of the approach proposed by the authors has been confirmed during experiments on the WS-Dream dataset. The results demonstrate that taking cultural factors into account can significantly improve the quality of recommendations, especially in cold-start environments. The integration of the matrix factorization method facilitates more accurate modeling of latent factors affecting user choice and allows recommendations to be adjusted according to the identified patterns. Incorporating cultural characteristics into the recommendation process outperforms conservative methods based solely on behavioral data and provides a more personalized approach to new users.

This article discusses methods of spectral analysis and application in automated information systems, in the frequency domain using the Fourier transform. The authors have developed mathematical models that allow formalizing control and information processing problems in applied areas. The study of the equations proves that the shift of the system in time and the subsequent application of the Fourier transform allows to simplify the analysis of processes and find optimal control actions. The application of spectral methods has made it possible to efficiently find solutions to control problems, especially in the presence of constraints on the system parameters and requirements for the smoothness of the control signal. The obtained expressions demonstrate that the correct choice of the function ũ^*(ω) taking into account its integrability on the whole frequency axis and finiteness of degree in a given interval allows to carry out accurate and effective control of the dynamic characteristics of the system. The analysis and obtained results show that taking into account these properties of the control function allow to minimize undesirable oscillations, providing smoothness of transient processes and exact compliance with the given boundary conditions. Algorithms of spectral data representation as signal filtering, algorithmic data transformation and time series analysis allowed the authors to highlight or remove certain frequencies in the signal. In existing automated data storage and processing systems, the use of spectral analysis helps to improve the speed of computation.

The aim of this study is to develop and validate a model for assessing the resilience of distributed systems that considers both the structural characteristics of the network and the dynamic behavior of connections. The proposed model combines graph analysis (based on the Erdős – Rényi model) and statistical modeling (Gilbert – Elliott model), integrating connectivity probabilities and successful connection metrics to evaluate network resilience. The primary objective was to create an approach capable of accurately describing real-world network processes and identifying potential points of degradation. The model was tested using a local Kubernetes cluster, where a test CRUD service was deployed under load for 24 hours. Collected metrics, including packet loss, latency, and throughput, were compared to the model’s predictions. The results showed minimal deviations between theoretical predictions and empirical data, confirming the model’s adequacy. The study concludes that the proposed approach can not only accurately describe current network processes but also serve as a foundation for decision-making regarding scaling and replication. The model’s flexibility ensures its relevance in scenarios involving changes in network topology or connection quality, making it applicable for analyzing modern distributed systems.

In this paper, the Berlecamp – Messy algorithm, its features and the relevance of using this algorithm for various tasks are discussed. A simple linear feedback shift register (LFSR) has been chosen and the general circuit of the register is presented. The Berlecamp – Messy algorithm has been implemented in the C# proramming language using the WTF platform, the graphical shell of the developed complex has been shown, the block diagram of the algorithm has been given and the program code has been presented. Demonstration of the complex operation on the example of bit stream of the RSLOS generator and comparison with the calculated values is carried out. The results of the work can be used for creation of more perfect data protection systems and training of future specialists, the developed pro-software complex and presented algorithms can be used in the educational process within the discipline “Cryptography” for students in the direction of “Information Security”.

Intelligent Transport Systems (ITS) are now being implemented to ensure optimal and safe road traffic. Increasingly, these systems use artificial intelligence to obtain characteristics about traffic flows. The number of sensors and transducers is increasing dramatically, resulting in higher loads on ITSs. Therefore, it is necessary to develop distributed monitoring systems with scalability and fault tolerance in mind. However, extensive testing is required before implementation. It is not possible to fully conduct such testing on real data due to various factors. Therefore, this paper proposes a tool for generating synthetic traffic flow data with subject matter specificity. The generation system is designed to be integrated into different systems, which will allow different ITS vendors to use it. This service fulfils the scalability requirements and is close to real data. The study proposes a scalable architecture of intelligent transport subsystem that fulfils the requirements of scalability and fault tolerance. As part of this work, a testbed is assembled and the proposed architecture is tested through the developed service of synthetic traffic flow state data generation.

The article presents a model of the knowledge base for managing the sustainable development of industrial ecosystems. The knowledge base is a semantic model that describes the industrial ecosystem and allows answering such questions of the subject area, the answers to which are not explicitly present in the base. The model, based on the ontological approach, integrates data on the interaction of ecosystem participants, processes, products and resources, and is aimed at optimizing these interactions to achieve sustainable development. The work identifies the classes of the model (the main entities of the subject area) and establishes the relationships between them, provides a composition of the functional modules of the knowledge base, which provides a fairly complete picture of industrial ecosystems and sustainable development management processes. The article presents testing of the model for assessing the dynamics of ecosystem sustainability based on the interaction of three companies. Modeling takes into account the influence of resources and external factors, which allows calculating the overall index of ecosystem sustainability. The results showed that companies with a high level of cooperation demonstrate a significant increase in resilience. The conclusion of the work emphasizes the need for further improvement of the model, taking into account real data and additional factors of uncertainty and variability inherent in industrial ecosystems.

This paper examines phenomena in quantum mechanics that may initially appear to violate the laws of thermodynamics but actually conform to quantum principles. The discussion includes phenomena such as the impulse tunneling effect (ITE), quantum tunneling that allows particles to pass through potential barriers; superconductivity, where electric current flows without resistance and the wave function collapse that occurs during the measurement of quantum systems. The Zeno effect, where a particle can remain in an excited state under constant observation, and quantum fluctuations related to vacuum energy, leading to the emergence of virtual particles, are also considered. The potential for effective solar energy utilization through ITE is highlighted, even in the presence of insufficient quantum energy in the solar spectrum. Despite the apparent contradictions with the laws of thermodynamics, these quantum phenomena underscore the uniqueness and complexity of the quantum world, enhancing our understanding of physics and demonstrating that quantum mechanics operates within its own principles without violating thermodynamic laws.

Goal. This article discusses the theoretical and practical aspects of applying natural language processing (NLP) models in business processes of organizations of various scales. The purpose of the research is to systematize and analyze the main directions of NLP models’ application in modern business, as well as to develop practical recommendations for their effective implementation. Model. The research is based on the work of foreign authors who conceptually studied the application of NLP models in relation to various business processes. The research methodology is based on a systematic analysis of scientific publications and industry reports, a comparative analysis of technological solutions and a structural and functional approach to the systematization of NLP applications. Conclusions. The analysis of the main tasks solved using NLP models in business is carried out, among which are the generation of text content, text classification, automation of customer support, information summarization, machine translation and personalization of marketing interaction. The technological pipeline for creating and training NLP models is studied with a detailed examination of tokenization processes, vector representation of data, and the application of the attention mechanism. The research results demonstrate the high potential of NLP technologies for optimizing business processes. It has been revealed that, despite the growing interest in these technologies, their full-fledged implementation into the business processes of domestic companies remains limited. Practical significance. Practical recommendations on a strategic approach to the implementation of NLP technologies are formulated, including step-by-step integration with measurable results, focus on solving specific business problems and the need for investments in staff training. An example of the application of the model to solve the problem of extracting information from text in Russian is shown. Social consequences. The widespread introduction of NLP technologies in business leads to significant changes in the employment structure, requiring the retraining of specialists and the creation of new competencies in the labor market. Originality/value. The study is valuable for business leaders, IT specialists and digital transformation specialists, and offers a comprehensive analysis of the possibilities of using NLP technologies in various sectors of the economy. The novelty of the work lies in the systematization of NLP models in business, taking into account Russian specifics and current market trends.