оптимізація

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.

The article proposes an innovative approach to optimize the routing process in distributed networks using machine learning techniques, specifically reinforcement learning. This method enables the adaptive determination of optimal data transmission paths based on current network conditions, enhancing overall performance and resilience to dynamic traffic fluctuations. The proposed approach dynamically adjusts to variations in network topology, traffic load, and node availability, ensuring efficient data flow management even in highly dynamic environments.

The increasing complexity of cyber threats requires the development of effective methods for detecting and classifying attacks in network traffic. This study analyzes the effectiveness of three popular machine learning algorithms: Random Forest, which is used for anomaly detection, Support Vector Machines (SVM), which performs cyber threat classification, and autoencoders, which are used for data preprocessing and deep traffic analysis.

A review of publications devoted to the mathematical simulation of the nanofiltration process was carried out, the advantages, limitations, and areas of application of various modeling approaches were determined. It was found that the most effective approaches are based on the extended Nernst-Planck equation, Donnan equilibrium, as well as methods of computational fluid dynamics and molecular dynamics. The use of software for solving nanofiltration simulation problems was considered.

The paper describes a constant time clustering algorithm that can be applied on wireless sensor networks. The scheme for rate control, scheduling, routing, and power control protocol for wireless sensor networks based on compressive sensing has been shown. Using network utility maximization formulations, cross-optimization solutions using Lagrangian multipliers in network access control and physical layers have been presented. The optimization solutions have been developed by solving the optimization model of network utility maximization.

The authors consider the features of multi-agent modeling for traffic optimization in the central areas of cities. While evaluating the unique challenges associated with the high concentration of vehicles, pedestrians and historical buildings, the potential of multi-agent systems to effectively solve the problem of congestion, safety and quality of life in urban areas is investigated. The potential of multi-agent modeling in the context of traffic management in the central areas of the city allows us to identify the key challenges and opportunities.