Computational nanotechnology

The article discusses the use of numerical methods to optimize the design elements of product cards. The discount block, one of the key elements significantly influencing sales, is selected as the object of study. The aim of the research is to improve the click-through rate (CTR) of product cards by analyzing and optimizing visual parameters such as color, font size, block placement, discount format, and device type. To achieve this goal, a regression model was developed to predict CTR for new parameter combinations without the need for full-cycle testing and to evaluate the significance of the analyzed parameters. The results show that the most impactful factors on CTR are background color, font size, and the placement of the discount block. The proposed approach reduces the number of required tests, accelerates the optimization process, and can be adapted to other design elements, such as call-to-action buttons or stock availability indicators.

Due to the ever-expanding threat landscape, the problem of timely identification of information security risks, their assessment, and, as a result, management of these risks remains urgent. The main components of all quantitative risk assessments are the frequency, or probability, of the realization of a risky event, and the amount of losses from the realization of the threat. The purpose of the work is to increase the accuracy in quantifying information security risks, develop a theoretical model that takes into account all the relationships between assets in the company’s information environment, and compile an effective set of risk management measures. To formalize the company’s information security risk assessment model, a set of security breach conditions for the company’s information environment was identified, consisting of elements characterizing the possible results of threat implementation for each asset. As a result of the development of the model, the relationship of assets and the versatility of threat scenarios are shown.

When perceiving complex, saturated images, the task arises not only to track where the user’s gaze is directed, but also to understand how he processes information on the screen, how he switches between objects and where his attention lingers longer. The effectiveness of perception is predetermined by the effectiveness of the interface and the information model built by the developer, the effectiveness of whose work is determined by the identified accents of the human operator’s attention. An eye tracking toolkit has been developed in Python version 3.10 with the connection of the libraries mediapipe, openCV and matplotlib with extended functionality aimed at improving the accuracy of interpretation of gaze behavior and improving the methods of presenting the collected data. The use of the developed toolkit allows us to determine the areas of interest of users, identify accents of attention, which can serve as a basis for constructing attention maps, which can subsequently help in creating an effective user interface.

The Doppler effect, arising from the relative motion between the source and the observer, plays a significant role in quantum computations, particularly in the context of decoherence and the state of qubits. In quantum systems where information is encoded in the states of qubits, changes in the frequency of photons caused by the Doppler effect can lead to coherence violations and a decrease in computational accuracy. These changes can cause state mixing and complicate the management of quantum interactions, increasing the probability of errors. Understanding and accounting for the Doppler effect is critically important for designing robust quantum systems, as it can manifest in various types of qubits, including photonic, atomic, and ion qubits. To minimize the impact of the Doppler effect, it is necessary to develop error correction methods and utilize technologies such as polarizing filters or feedback systems. Thus, studying the Doppler effect deepens our understanding of the mechanisms of decoherence and contributes to the creation of more stable and efficient quantum computing systems.

This paper presents state-of-the-art image processing and structural analysis software tools that use GPU parallel programming to achieve substantial performance gains. The software suite combines advanced preprocessing techniques, object identification methods, clustering algorithms, and analysis tools to facilitate efficient and precise analysis of complex imaging datasets. The case studies illustrate the software’s versatility and effectiveness across diverse scientific domains, including materials science, biological research, and astronomy. By exploiting GPU parallel programming, the tools deliver performance improvements of 5–20x compared to traditional sequential programming, enabling real-time visualization and expedited data processing. The intuitive user interface empowers researchers to fine-tune parameters, visualize results, and interpret data with ease, streamlining the research workflow. The broader impacts of these tools include accelerating scientific discovery, enhancing data analysis accuracy, and driving innovation across diverse scientific fields. A notable example of their effectiveness is the processing and analysis of electron microscopy images of amorphous alloys. The developed algorithms and software tools demonstrate promising results in this area, facilitating detailed studies of atomic structure and degree of orderliness.

The goal of this study is to develop and verify a monitoring model for reliability and availability in distributed systems, built on probabilistic component characteristics and accounting for dependent failures. Modern distributed systems require accurate failure prediction methods that can account for complex dependencies between nodes and support reliable performance under high loads. Traditional approaches based on empirical data analysis often fall short in predicting system states under changing loads, which limits their applicability. In this research, the developed probabilistic model underwent verification using numerical simulation and accuracy assessment through Kullback–Leibler divergence and mean squared error (MSE), confirming its accuracy and practical value. The model’s versatility was proven experimentally, demonstrating its ability to adapt to various types of distributed systems while providing precise real-time predictions of availability and resilience. Numerical experiments showed that the proposed model can be a reliable tool for managing fault tolerance and load balancing. Thus, the developed model is an effective solution for enhancing the reliability of distributed systems, exhibiting a high degree of versatility and making it valuable for a wide range of applications.

Subject of the Study. This article explores the use of Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM) networks, to improve the effectiveness of Intrusion Detection Systems (IDS). The study emphasizes the work of optimizers to enhance the accuracy of detecting network attacks and provides a comparative analysis of various optimization algorithms using the NSL-KDD dataset. Method. The article proposes an RNN-LSTM-based approach for detecting intrusions in network traffic. Seven different optimization algorithms were evaluated, including Adamax, SGD, Adagrad, Adam, RMSprop, Nadam, and Adadelta. The method involves a comparative analysis of their performance with varying hidden layer sizes. Main Results. The experiment methodology included training the RNN-LSTM model with hidden layer sizes ranging from 50 to 100 over 500 epochs. The Adamax optimizer achieved the highest accuracy of 99.79%, while Adadelta had the lowest accuracy at 97.29%. Additionally, SGD demonstrated the best True Positive Rate (TPR), while Adamax showed the lowest False Alarm Rate (FAR). The study evaluated metrics such as accuracy, TPR, FAR, precision, and F1-score, with Adamax standing out for its overall performance. Practical Significance. The article is relevant to professionals in the fields of cybersecurity, network security, and IDS development. This article provides valuable insights for enhancing IDS configurations to improve the detection and mitigation of network intrusions.

Analyzing complex information models, constructed from any collected data, is a challenging task. In recent years, new methodologies have emerged and been proposed to address various problems in this field. However, there is still a need for new, efficient, and user-friendly methods for data presentation and information modeling. This paper proposes a method for creating a nested structure based on fuzzy cognitive maps. In this method, each concept can be represented as another fuzzy cognitive map through clustering, which provides a more detailed and accurate representation of complex data and increases the convenience and efficiency of analyzing such information models. This nested structure is then optimized by applying evolutionary learning algorithms. Through a dynamic optimization process, the entire nested structure based on fuzzy cognitive maps is restructured to obtain important relationships between map elements at each level of nesting, as well as to determine the weight coefficients of these relationships based on available time series. This process allows for the discovery of hidden relationships between important map elements. The article proposes the application of this nested approach using the example of a fuzzy cognitive map of the influence of various social factors on becoming homeless.

The relevance of this article is due to the interest in simulation modeling in the context of doing business, as well as in order to improve it. Modeling gives you an in-depth understanding of how a business works, allowing you to experiment with it in a secure digital environment and identify bottlenecks that require optimization. At the same time, one of the most important steps in building a model is to adjust the source data. If you do not take into account the operations typical for the company, the result of the experiments will not be plausible in the model and, therefore, inapplicable in practice. It is also important to define an approach to simulation modeling, since the result of modeling after an incorrect choice can take a long time and turn out to be unprofitable. A modern workforce that supports any logic is pre-configured to create simulation models that regulate the real tactics and operation of seaport container terminals: ship sections, cargo transportation, transportation by various modes of transport and personnel composition. As part of the study, the reconstruction of the antenna path was considered, and a description of its special reconstruction in the Anylogic center was considered using the example of the container terminal of the Vladivostok commercial port.

The article considers the problem of practical modernization of IT service management system in software development companies. This problem has recently been widely considered in the works of world scientists [1–5; 13–15]. To study the needs of modernization, typical business process diagrams of the developer company (BPMN 2.0) are given. Based on the analysis of the business processes and the shortcomings identified in them, the components of an architectural microservice solution for modernization in the area of incident handling are developed and given in schematic form. As a result, the microservice architecture of the search module of the ITSM system is obtained, which is given in the form of a model with explanations in the article. As a development of the research, it is planned to carry out pilot tests of the developed architecture in solving a number of applied tasks with subsequent evaluation of the obtained results. The proposed modernization variant is characterized by universality and can be considered as a solution in each case of ITSM use in a production enterprise. The article will be useful for IT-specialists implementing and maintaining ITSM systems.

Electronegativity and chemical hardness are important concepts in chemistry that influence the structure, properties, and reactivity of substances. Electronegativity defines an atom’s ability to attract electrons in a chemical bond, which affects the polarity and stability of molecules. Chemical hardness, on the other hand, characterizes a substance’s resistance to changes in its electronic structure and its response to external influences. These two parameters are interconnected and play a key role in understanding the behavior of chemical compounds, particularly in the context of superconductors, catalysts, and materials with unique properties. Understanding the relationship between electronegativity and chemical hardness may contribute to the development of new materials with tailored characteristics and enhanced properties.

This article examines the phenomenon of fractals and their role in understanding the structure of the universe. Fractals are complex geometric structures characterized by self-similarity, finding applications in various fields of science, from mathematics to biology. Examples of fractals in nature are provided, including galaxies, clouds, the nervous system, and natural landscapes. The discussion highlights how fractals assist in modeling complex systems, analyzing data, and understanding the evolution of different structures. The article emphasizes the importance of fractals as a tool for studying natural processes and their significance for further research in quantum physics and chaos theory.