The article analyzes the tasks and challenges of managing complex corporate networks that arise during the migration from a perimeter-based security model to a Zero Trust Architecture (ZTA). It is established that neglecting these issues may lead both to a decline in service quality within microsegmented network infrastructures and to increased risks associated with the excessive complexity of firewall rules.
The object of the study is the process of organizing a descriptor repository in content-based image retrieval systems. The subject of the study is a method of numerical optimization of descriptor clustering in a multidimensional space. The aim of this work is to develop a clustering optimization method in the Multidimensional Cube model to improve search efficiency. The core idea is to ensure a more uniform distribution of descriptors across clusters by adjusting interval boundaries in each dimension, which reduces imbalance in cluster density and improves retrieval performance.
Lignite (brown coal) is a promising source of humic acids (HAs) and toluene-soluble extracts (bitumen "A"), which have applications as soil conditioners/biostimulants (HAs) and hydrophobic coatings or polymer additives (toluene extract). This study optimized their sequential extraction from Ukrainian lignite, evaluating yield trade-offs and structural properties. Four extraction variants were tested. Conventional toluene-first extraction (Variant 0) yielded the highest toluene extract (14.86 wt. %), but lower HAs (41.0 wt.
Modern info‑communication technology (ICT) infrastructures such as content‑delivery networks (CDNs) must continuously tune low‑level parameters to deliver high performance under variable and non‑stationary network conditions. This paper investigates how online controlled experiments—including classical A/B tests and adaptive multi‑armed bandit (MAB) algorithms—can be used to optimise CDN node selection. We formalise the optimisation problem as minimising a network‑performance objective of average latency, one of key metrics used to measure network performance.
The paper considers methods for optimizing training samples for deep learning algorithms through the use of random point processes, such as Matern of the first and second types, Gibbs, Gaussian, and Poisson processes. An approach to reducing training data without sacrificing informativeness is proposed, enabling a decrease in computational costs and mitigating the risk of overfitting.
The numerical investigation of stagnation point flow past a stretching or shrinking cylinder in carbon nanotubes with the presence of hydromagnetic effects is examined. This study has been solved by ordinary differential equations obtained using the similarity transformation that transformed from the governing equations along with the boundary conditions, then implemented the bvp4c solver in MATLAB software platform, ensuring accurate results. Considering two types of base fluids, namely water and kerosene. Also, single-wall and multi-wall types of carbon nanotubes a
This article discusses modern approaches to organizing continuous integration (CI) and continuous delivery (CD) processes in software development using artificial intelligence (AI) technologies. The historical development of CI/CD is analyzed, along with their role in ensuring high-quality software, the main advantages and disadvantages of traditional approaches, and the prospects for integrating AI technologies to automate and optimize these processes.
The article formulates the advantages and challenges of implementing circular economy princi- ples in the pharmaceutical industry. The main strategic aspects of optimizing business processes in the context of sustainable development are substantiated. The results of the study highlight the specifics of implementing circular economy principles in the pharmaceutical industry by updating the environmen- tal, economic and social benefits of such a transition, as well as analyzing potential opportunities and challenges along the way.
In the current context of digital transformation within the energy drinks market, the use of digital technologies has become a crucial tool for enhancing the efficiency of business processes, marketing strat- egies, and consumer engagement. However, despite considerable opportunities, the widespread imple- mentation of digital instruments in this sector faces several challenges that require both academic analysis and practical solutions. One of the key issues is the adaptation of energy drink producers' business models to the realities of the digital environment.
The article explores the administration of human resource potential using the case study of LLC ‘Trade House “Galka”. It examines the theoretical foundations of human resource management, key ad- ministration methods, and major factors influencing the efficiency of personnel policies within the com- pany. A general analysis of the company’s production and economic activities is conducted, identifying strengths and weaknesses in the existing human resource management system.