Volume 7.
Issue 02.

The article examines challenges encountered in the process of establishing charging station networks for electric vehicles, proposes solutions to address them, and focuses on identifying barriers such as standard incompatibility, difficulties in implementing and using different types of devices, and power limitations.

The methodological approach is based on the analysis and comparison of charging infrastructure models implemented in various countries, addressing current issues and development prospects. Based on the identified barriers, proposed solutions include equipment standardization, the creation of intelligent station management systems, and the modernization of power grids to meet growing demands.

The findings indicate that the development of a charging station network requires consideration of multiple factors: technical standards, economic feasibility, and environmental safety requirements. The conclusions drawn will be useful for infrastructure developers, researchers in the field of sustainable transportation, and government entities involved in environmental and transportation policy.

The conclusion emphasizes that overcoming technological barriers in creating an accessible and efficient charging station network is essential for the widespread adoption of electric vehicles, which will contribute to the development of a sustainable and environmentally friendly transportation system.

This study investigates the synergistic effects of static mixers on biogranule formation in the aerobic treatment of textile wastewater. Static mixers, commonly used in various industrial applications for their ability to enhance fluid mixing, are employed to optimize the microbial aggregation process during bioremediation. The research evaluates how different configurations and operating conditions of static mixers influence the formation, size, and stability of biogranules in aerobic reactors treating textile wastewater. Experimental results demonstrate that the enhanced mixing leads to improved microbial growth, faster substrate consumption, and more stable biogranules. The findings suggest that incorporating static mixers into aerobic systems can significantly improve the efficiency of textile wastewater treatment, offering a potential solution for the textile industry's wastewater management challenges.

At this time, the use of artificial intelligence (AI) for IT service delivery becomes a key modernization strategy that aims to move to a more efficient operation, decrease the cost of providing services, and increase its quality. The focus of this paper examines the AI driven predictive analytics and intelligent automation's ability to bring transformation into the IT service processes for the purpose of optimization. This research achieves this by conducting an extensive review on literature available about AI in the IT service industry and performing data driven analysis indicating key domains where AI can correctly anticipate and preempt IT service disruptions, allocate resources in an optimal manner and carry out routine tasks, helping increase overall efficiency substantially. To support the methodology, service metrics (incident response time, system downtime, etc.) are considered using their quantitative forms and analyzed using statistical tools to measure the impact of AI on these metrics. The findings show major improvements like predictive maintenance, automated issue resolution, and service personalization can be achieved with right implementations of AI technologies. This paper makes an addition to the body of literature, by conducting an in-depth investigation on the role of AI in changing IT service delivery, and presents practical insights to industry practitioners and regulators. The study proves that the adoption of modern AI technologies help the modern IT infrastructure remain competitive and develop as a drive to operational excellence.

The paper discusses the process of building a fully automated pipeline for serverless deployment using AWS Lambda, Terraform and GitHub Actions. These technologies allow developers to create infrastructure without server management, manage it as code, and automate CI/CD processes. The article discusses the basic principles of using AWS Lambda to perform functions in a serverless architecture, Terraform for declarative infrastructure management, and GitHub Actions for automating the deployment process. The configuration steps are described, including creating and configuring IAM roles, connecting via API Gateway, and monitoring using AWS CloudWatch. The main focus is on the automatic scalability and flexibility of such solutions, as well as problems related to debugging and testing. The work highlights the benefits of integrating these tools to improve DevOps processes and accelerate application development.

The increasing deployment of renewable energy sources, decentralized generation, EV, and dynamic consumers has made modern smart grids complex cyber-physical systems, making conventional energy management methodologies ineffective for real-time operations and decision-making. Currently used optimization algorithms work within closed and restricted environments, possess weak flexibility to uncertain conditions, and lack autonomy and predictive abilities for multi-layer operation. Therefore, the present study suggests a sophisticated Artificial Intelligence and Predictive Analytics-based Energy Management Model that performs continual forecast of grid demand, renewable generation, storage behavior, and network restrictions while executing adaptive optimization. The new method uses innovative Multi-Layer Cognitive Energy Twin, which builds a constantly evolving virtual representation of grid behavior; Adaptive Predictive Resilience Index for early prediction of stability, and Self-Evolving Collaborative Intelligence Engine which dynamically modifies operational strategies without following specific regulations. Also, the new system will use a Cross-Domain Energy Knowledge Fusion technique, which will learn from consumer preferences, environment, markets, and infrastructure, and will generate optimal strategies of energy management. Instead of reacting to unpredictable events, the proposed solution predicts grid states in the future and autonomously chooses energy management strategies, which increase efficiency, sustainability, resilience, and economy.

The article examines methods for ensuring data protection in mobile applications developed using the React Native framework, widely employed for creating cross-platform solutions. The introduction highlights the importance of ensuring security amid the growing number of mobile device users and the increasing volume of personal data processed by applications. Developers are tasked with preventing data breaches and unauthorized access to sensitive information, which requires meticulous attention at all stages of development.

The objective of the study is to analyze data protection methods. The discussion focuses on encryption, user authentication, API security, and the use of secure storage for sensitive data. Particular attention is paid to the features of applications built with React Native and the vulnerabilities inherent to this framework. The study includes a review of existing protection methods and practical implementation of recommendations in real-world projects.

The findings indicate that a comprehensive approach is essential to ensure the security of mobile applications on React Native. Implementing biometric authentication, including fingerprint and facial recognition, as well as data encryption on both client and server sides, is mandatory. Protecting interactions to prevent attacks aimed at intercepting or modifying data is also of critical importance.

The information presented in the study will be valuable to developers, information security experts, and professionals in mobile technologies.

The article is aimed at researching and describing effective approaches and methods for quickly detecting and identifying errors in web applications while operating in production environments. This, in turn, is due to the fact that in modern conditions, the speed of detection and elimination of defects is important to ensure reliable operation of web applications.

The relevance of this topic is driven by the increasing transition of business processes to the online space. As more companies and human activities become dependent on software reliability, defects in commercial software products can lead to significant financial losses, reputational risks, and loss of user base. The growing complexity of web applications and their increasing role in critical business operations further emphasizes the importance of robust error detection methodologies. Therefore, timely detection and elimination of errors has become a vital business necessity.

The methodology described in this article represents a promising approach to rapid error detection in web applications, offering a systematic framework for monitoring and managing software errors in real-time. The methodology includes detailed recommendations and practices for identifying errors efficiently, even in software products handling high-volume error streams with substantial user loads.

The article will be useful for software developers, engineering managers, DevOps specialists, and researchers in the field of analysis and diagnostics of web applications. It provides a description of the techniques and tools used to improve the efficiency of working with web applications and improve their quality and security.

This study examines the potential application of CAD systems to optimize design and prototyping processes in engineering and manufacturing. The objective is to conduct a comprehensive analysis of the potential of these systems to enhance the efficiency of development and prototyping. Particular attention is given to their functional capabilities, including integration with other technological platforms, the creation of digital prototypes, and automatic error detection at early development stages. The methodological approach involves analyzing scientific studies available on the Internet.

The results of the reviewed studies confirm that the implementation of CAD systems reduces development time and improves product quality. The use of 3D modeling and process simulation tools enables the identification of defects at initial stages, preventing their propagation during production.

The practical significance lies in the fact that the proposed approaches may be of interest to engineers, designers, and project managers seeking to increase development efficiency, reduce costs, and improve production processes at all stages of the product life cycle—from concept creation to prototype manufacturing.

Thus, the use of CAD systems for automating design and prototyping serves as a tool for achieving a high level of productivity and design precision.

Business intelligence receives its revolution from artificial intelligence technologies in IT sector information systems which turn raw big data into strategic action insights for organizational leaders. This research evaluates AI-powered technologies that assist BI frameworks and their ability to improve data analysis and predictive forecasting as well as automate processes. The study performs a full examination of existing documentations alongside industrial implementations and case study evaluations to demonstrate AI-based BI applications for operational efficiency along with expenditure reductions and fact-based decision-making improvements. The analysis methods of this paper use validated secondary data taken from peer-reviewed journals industry reports and case studies which demonstrate principal trends and effects. AI-based BI solutions strengthen decision support because they provide immediate contextual information. The research demonstrates major uses of AI technology which includes machine learning patterns through algorithms as well as natural language processing sentiments and AI dashboard visualizations. Despite these accomplishments the study presents obstacles which involve data security issues and system integration difficulties together with a lack of qualified personnel for AI control operations. This paper introduces innovative BI strategies along with their impact on IT decision-making processes as the main novelty in addition to filling existing research gaps. The research presents implementable guidelines which assist organizations as well as policymakers and academics to leverage AI technology for growing sustainably together with competitive advantage.

This article examines the features, challenges, and potential solutions associated with the integration of robotic systems into existing infrastructure. The relevance of this topic is driven by the widespread implementation of robotics across various sectors, including industry, logistics, agriculture, and other fields. The objective of this study is to systematize existing approaches and perspectives on integration processes, analyze key contradictions in scientific literature, and identify underexplored aspects that hinder the effective deployment of these technologies.

Discrepancies in the literature are linked to the lack of standardization in system architectures, limited real-world validation of control algorithms, and insufficient attention to legal considerations. It is concluded that successful integration requires a systematic approach encompassing standardization, the development of advanced control algorithms, adaptation of cloud technologies, and the mandatory consideration of social factors.

The author's contribution lies in structuring modern trends in integration strategies, including the shift towards modularity, scalability, active use of artificial intelligence, standardization, cloud-based solutions, and hybrid interaction systems. Additionally, research gaps are identified. The presented findings will be valuable for developers of robotic solutions, automation researchers, and enterprise managers planning the implementation of robotic systems.

This article presents an analysis of the economic growth and development indicators of cotton-textile clusters in the Namangan region. The study examines the key factors influencing the formation and expansion of these clusters, including production capacity, employment rates, investment inflows, and export performance. Special attention is given to the role of government support, infrastructure development, and technological advancements in enhancing the competitiveness of the textile industry. Findings indicate that the integration of cotton farming with textile manufacturing has contributed to increased value-added production, improved supply chain efficiency, and enhanced international market penetration. The study also highlights challenges such as raw material supply fluctuations, market competition, and environmental considerations, offering recommendations for optimizing the cluster-based approach to foster long-term industrial growth in Namangan.

The rapid evolution of automotive systems toward higher levels of automation, electrification, and software-defined functionality has fundamentally transformed the landscape of functional safety. ISO 26262 has emerged as the cornerstone standard governing automotive functional safety, providing a structured framework for managing risks associated with electrical and electronic systems. However, the increasing integration of complex software architectures, semiconductor-based platforms, autonomous driving features, and artificial intelligence-driven decision-making challenges the traditional interpretation and application of the standard. This research article presents an in-depth, theory-driven, and critically elaborated examination of ISO 26262 compliance across the automotive development lifecycle, with particular emphasis on Automotive Safety Integrity Level (ASIL) allocation, decomposition, fault analysis, dependent failure assessment, safety monitoring, and the implications of intelligent and autonomous system behaviors. Drawing strictly from the provided scholarly and industrial references, the article synthesizes conceptual modeling approaches, algorithmic ASIL allocation techniques, bottom-up and top-down safety decomposition strategies, hardware reliability concerns, and emerging safety governance paradigms. The study adopts a qualitative, analytical methodology grounded in comparative literature interpretation, conceptual reasoning, and systemic analysis. The findings reveal that while ISO 26262 remains robust as a foundational safety framework, its practical application increasingly depends on advanced modeling, automation, and adaptive safety reasoning to address system complexity. The discussion highlights theoretical tensions between deterministic safety assurance and adaptive system behavior, identifies limitations in current compliance practices, and outlines future research directions necessary to ensure trustworthy and scalable safety assurance for next-generation automotive systems. This work contributes a comprehensive academic resource for researchers, engineers, and policymakers seeking to understand and advance functional safety in the era of intelligent mobility.

This article examines modern methodological developments in the field of construction schedule optimization as a tool to improve the management efficiency of related projects. The relevance of the topic is justified by the urgent need to adapt traditional planning approaches to the realities of digitalization, the growth of project scales, and the complexity of infrastructure design. The aim of the study is to systematize and characterize approaches that ensure the minimization of time and resource costs (without compromising the quality of construction work). Following a review of contemporary scientific literature, discrepancies have been identified regarding the choice of optimal methods for different types of projects: on one hand, research shows high effectiveness of automated technologies (such as 4D BIM), while on the other hand, their implementation is often significantly limited by economic and organizational determinants. The conclusion is made that successful optimization actions in this area require the integration of classical methods (CPM, PERT) with adaptive algorithms and mathematical models. The author's contribution lies in the formulation of recommendations for improving the efficiency of construction schedules (a specific sequence of actions is proposed, taking into account both external and internal challenges). The materials presented will be useful for project managers, engineers, and researchers focused on improving the efficiency of construction processes.