оптимізація

The article discusses the problem of instability of test selectors in the process of automated testing of web applications. It raises the issue of selectors’ adaptability to changes in the DOM structure, which is critically important in the development of modern dynamic web interfaces. A comparative analysis of three approaches to selector generation is conducted: manual (via Chrome DevTools), semi-automated (using DevTools), and automated using graph neural networks (GNN).

The article examines the theoretical and practical foundations of developing a system for optimizing import activity within enterprises. The essence of the definition ‘optimization of import activity’ is defined as a process aimed at improving the management of foreign economic operations in order to minimize costs, enhance efficiency, adaptability, and competitiveness under conditions of global market transformation.

Interaction with customs authorities is a crucial component of successful foreign economic activity, especially for companies involved in regular import operations. The efficiency of such interaction directly influences the speed, cost, and reliability of international trade processes. Therefore, the goal of this study is to analyze and improve the mechanisms of customs interaction in the context of the activities of LLC ‘Lviv Auto Hub’ – a company engaged in systematic import of vehicles into Ukraine.

This paper proposes a cyber-physical modeling framework that enables a planner to optimally position implantable medical devices in therapeutic systems that operate interstitially. To inspire a model, we use applications such as interstitial photodynamic therapy and brachytherapy, and frame the problem as constrained packing of cylindrical objects with spatial and angular feasibility conditions. An approach combines anatomical geometry, device orientation restrictions, and tissue-specific feasibility to create an integrated optimization model.

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.

The article focuses on the importance of website loading speed. It states that the faster the loading time of web pages, the better the conversion, search engine rankings can be, etc. It is noted that waiting for a website to load can lead to a negative user experience, which reduces trust, leads to a loss of reputation, lower search engine rankings, reduced profits, etc. Based on the analysis and our practical experience in website development, we have systematised recommendations by category and provided practical advice on how to speed up website loading.

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.