Vol. 11, No. 02 [February 2025]

The widespread application of tracked vehicles in complex terrains, especially in sandy environments, has made their trafficability a hot research topic. Based on the Bekker terrain model and the Janosi-Sankey shear model, combined with experimental analysis, this paper explores the trafficability of tracked vehicles in sandy environments. The study finds that optimizing the design parameters of the tracks can significantly improve the vehicle's trafficability, providing important references for the design of tracked vehicles in complex terrains.
Keywords: Tracked vehicles; Terramechanics; Trafficability; Sandy environments

This article examines the role of artificial intelligence (hereafter referred to as AI) methods in strengthening the protection of personal data in large-scale information systems. The study focuses on analyzing the capabilities of deep learning for detecting cyber threats and integrating modern security technologies. A comprehensive review of the theoretical foundations of deep learning, key modern AI technologies, blockchain solutions, and advanced computing is conducted, along with a comparative analysis of traditional data protection methods versus the aforementioned tools. The application of a comprehensive approach that combines the high accuracy of deep learning algorithms with mechanisms for protecting confidential data ensures compliance with regulatory requirements (e.g., GDPR, CCPA) and maintains a balance between effective attack detection and privacy preservation. The research findings confirm the hypothesis that integrating AI with security technologies opens new prospects for the development of scalable and reliable cybersecurity systems in a rapidly evolving digital environment. The insights presented in this article will be of interest to other researchers, cybersecurity specialists, and IT architecture developers seeking to integrate interdisciplinary approaches to counter modern digital threats.
Keywords: artificial intelligence, deep learning, cybersecurity, personal data, differential privacy, federated learning, big data.

The article examines new opportunities for business entities arising from the integration of artificial intelligence (AI) into customer relationship management (CRM) systems. The modern business environment is characterized by increasing complexity in managing customer relationships, driven by the growing volume of data, the diversity of communication channels, and the increasing need for personalized interactions. In this context, the implementation of AI in CRM systems becomes not only a strategic advantage but also a significant factor in improving the operational efficiency of business entities.
The relevance of the topic is due to the fact that AI-CRM enables process automation, consumer behavior prediction, enhanced personalization, and improved service quality. However, academic discussions reveal significant contradictions regarding the scalability of such solutions. The objective of the study is to determine the prospects for AI integration into CRM and identify key challenges. An analysis of publications has established that the main barriers to implementation include technological complexity, organizational resistance, and ethical aspects of data processing.
The study concludes that the successful adoption of AI-CRM requires a systematic approach based on algorithmic transparency, corporate structural flexibility, and the development of regulatory frameworks. The author's contribution lies in systematizing contemporary approaches to AI utilization in CRM and identifying methodological gaps. The materials presented will be useful for marketing professionals, customer experience managers, CRM platform developers, and researchers focusing on digital business transformation.
Keywords: analytics, automation, business, artificial intelligence, customer experience, machine learning, personalization, prediction, data management, digital transformation.