Managing a front-end development team does not concern writing clean code or following rigid processes exclusively — it is a mix of engineering precision and people skills. This paper takes a closer look at how those two elements come together and offers a set of practical approaches drawn from real experience and recent research. Instead of sticking only to the technical side, the study pulls in ideas from agile leadership, team psychology, and modern software practices to give advice that actually fits how front-end teams work today. Key ideas that keep surfacing include shared ownership, creating a safe space for open communication (psychological safety), and leadership styles rooted in service and ethics. Continuous integration and deployment (CI/CD) also plays a big role. What is especially worth noting is how things like code reviews and automated testing — which are usually thought of as purely technical tasks — can double as learning moments and mentoring tools. They offer a chance for developers to support each other, grow together, and build a stronger team culture along the way.

Cloud-based Software-as-a-Service (SaaS) solutions are transforming the hospitality sector at a fast rate. They enable delivering services 30% faster and boosting guest satisfaction by 25% by means of real-time personalization and automation. This paper examines how SaaS impacts operational efficiency, guest experience, alignment with ESG, and strategic agility in the hospitality sector. With increasing customer demands and sustainability regulations, hotel brands are moving away from legacy systems to new, subscription-based solutions that are simple to scale and less environmentally unfriendly. This paper demonstrates, via a literature review, comparisons, and actual cases such as HotelKey, CitizenM, and Marriott, how SaaS in hospitality enables agility, digital inclusion, and ethical conduct in a post-pandemic world.

This paper examines how open source helps shape the frontend world by pointing out its part in fast-tracking progress and setting uniform client-side web standards. Its importance comes from massive growth—more than 5.2 billion actions on GitHub and over 2.5 million packages on npm—the community’s move toward TypeScript, and React's supremacy. This work tries to define a broader model of open source help that covers not just code but also CI/CD setups, plugins, translated docs, and even meetup groups, while checking how this affects the lifecycle of frontend tools plus standards. What makes this work new is bringing together info from GitHub Octoverse, npm Registry, corporate OSPO reports using a method unifying descriptive stats, types of contributions, network analysis on pull-request workflows, assessment of ecosystem strength through bus-factor measures, as well as maintainer burnout metrics. The proposed three-tier participation model—individual maintainers, corporate contributors, and institutional bodies (OpenJS Foundation, TC39)—enables the description of mechanisms for idea generation, resource scaling, and API standardization. Key findings demonstrate that open source contributions deliver unprecedented speed and flexibility in adopting innovations: the median pull-request merge time is 9 hours, large-scale events such as Hacktoberfest attract tens of thousands of newcomers, and the “Vite + Vitest + Storybook” toolchain sets the DevEx standard, enhancing the network effect of package publication and consumption. It also exposes systemic risks: 60% of maintainers are burned out, and 65% of projects have a low bus factor—sustainable funding, OSPO initiatives, and mentorship programs for maintaining ecosystem health. This article will help OSS project leaders, DevEx specialists, and OSPO teams strategize long-term support and scaling for open frontend tools.

This article examines the integration of autonomous vertical delivery systems (AVDS) for high-rise buildings in cities, while discussing the last-mile challenges that plague traditional elevator systems, which increase operational costs and CO₂ emissions. It is precisely due to this e-commerce and online food delivery boom that the study becomes relevant; modern times see up to 85% of a courier’s delivery cycle time as vertical movement inside a building—skyscraper situation kills all benefits brought by digitally optimized routing; enormous economic and reputational losses happen to both developers and delivery services. The novelty of this research lies in proposing full automation of vertical delivery based on a patented gateway for receiving couriers’ carts, a modular silent conveyor, and integrated microlockers on each floor, eliminating elevator frustration and ensuring end-to-end digital parcel tracking. The solution is easily scalable to any high-rise parameters and integrates with the IT infrastructure of major delivery operators. Following AVDS deployment, the courier time inside the building is reduced to less than one minute, empty elevator trips decrease by one-third, and the building’s carbon footprint is reduced by 3–4%. Econometric analysis reveals an 8.6% increase in tenants’ lease-renewal willingness and a 2.43 percentage point reduction in vacancy, while apartment values rise by USD 4,000–6,000 due to improved Environmental, Social, and Governance (ESG) metrics. This article will help developers, property managers, and logistics operators who want to cut costs and boost service quality in tall building projects.

The integration of Artificial Intelligence in Business Intelligence systems has fundamentally transformed enterprise analytics capabilities, enabling sophisticated pattern recognition, predictive modeling, and automated decision-making processes. However, the opaque nature of many AI algorithms presents significant challenges in business contexts where transparency, accountability, and regulatory compliance remain paramount concerns. This comprehensive technical review examines the role of Explainable AI in addressing these critical challenges, providing detailed insights into current methodologies, implementation frameworks, and practical applications across enterprise analytics environments. The content explores theoretical foundations distinguishing interpretability from explainability, emphasizing their crucial roles for different stakeholder groups within organizations. Technical frameworks encompass model-agnostic and model-specific methods, including LIME, SHAP, and attention mechanisms, alongside implementation tools ranging from open-source libraries to enterprise platforms. Real-world applications demonstrate XAI effectiveness across financial services, healthcare, retail, manufacturing, and human resources sectors, highlighting regulatory compliance benefits and stakeholder trust improvements. Current challenges include computational complexity, explanation fidelity, multi-modal data integration, and scalability issues, while emerging trends focus on automated explanation generation, interactive interfaces, and causal reasoning methods. Regulatory and ethical considerations address compliance evolution, bias detection, and fairness metrics, while technical advancements explore foundation model interpretability and privacy-preserving techniques.

This article examines strategies for risk management and regulatory compliance in the context of modernizing legacy IT infrastructure. The relevance of the topic arises from the growing need to integrate new technological solutions in environments characterized by limited flexibility, high maintenance costs, and technical debt—factors that contribute to operational, technical, and regulatory risks. The study analyzes the current state of legacy infrastructure, identifies key threats and vulnerabilities, and develops methodological approaches including modular migration, AI-driven analytics, the adoption of cloud technologies, and the integration of advanced security measures to ensure compliance with regulatory frameworks such as GDPR and HIPAA.
The scientific novelty lies in proposing a new perspective on integrating strategic planning, change management, and modern security technologies to reduce operational costs and improve business resilience. This perspective emerged through a critical review of existing literature. The author’s hypothesis suggests that a comprehensive approach to modernizing legacy IT infrastructure—grounded in modular transition to cloud solutions and automated security monitoring—will lead to a reduction in risk and an increase in the efficiency of business processes.
The findings of this study may be of interest to IT management professionals, risk and compliance officers, and researchers seeking to integrate the latest analytical methods into the evaluation and modernization of inherited IT systems. The presented material is also expected to be useful to other scholars developing theoretical models for managing complex IT environments, as well as to practitioners implementing strategies for mitigating operational and regulatory risks amid continuous technological and legal transformation.

Fixed-income index construction faces significant challenges due to reliance on manual processes that struggle to meet the demands of increasingly complex and volatile financial markets. The global fixed-income market encompasses diverse instruments across government, corporate, municipal, and securitized debt sectors, requiring sophisticated processing capabilities that manual approaches cannot efficiently deliver. Contemporary index construction involves extensive data sourcing from multiple terminal feeds, dealer networks, and regulatory sources, followed by complex normalization processes including currency standardization, credit rating harmonization, and maturity calculations. These manual processes introduce substantial vulnerabilities, including high error rates, processing delays, and scalability constraints that impact operational efficiency and index accuracy. Modern workflow orchestration technologies, including Apache Airflow, Dagster, and Prefect, offer transformative solutions by automating previously manual processes through sophisticated task management, fault-tolerant execution, and real-time processing capabilities. Automation implementation demonstrates dramatic improvements in processing speed, error reduction, and operational resilience while enabling resource reallocation toward strategic activities. Advanced artificial intelligence and machine learning technologies present unprecedented opportunities for dynamic index weighting optimization through reinforcement learning algorithms and anomaly detection systems that enhance data quality and market intelligence. The evolution toward automated index construction represents a fundamental transformation in financial market infrastructure, enabling institutions to maintain competitive advantages while meeting regulatory requirements and client expectations in rapidly evolving market environments.

In the present work a comprehensive comparative analysis of the three leading platforms for organizing message streaming — Apache Kafka, Amazon Kinesis and RabbitMQ — is performed with the aim of identifying their architectural features, operational strengths and limitations under conditions of peak loads and stringent latency requirements. The study relies on a comprehensive methodological approach, including a systematic review of current scientific publications, the conduct of comparative performance measurements in laboratory settings and the synthesis of practical case studies of integrating the systems under consideration into real IT landscapes. The obtained results demonstrate that a reasoned choice of platform for stream processing depends on a multitude of interrelated factors: the volume of messages processed, the required throughput metrics and maximum response time, the preferred deployment model (on-premises solution, cloud service or their hybrid), the capabilities for seamless integration with existing services and infrastructure, as well as the project’s budgetary constraints. On the basis of the conducted analysis a unified decision-making methodology is proposed for selecting tools for streaming data processing, adapted to the tasks of data engineers, distributed systems architects and researchers of high-performance information platforms. The material is of practical interest to specialists designing fault-tolerant and scalable distributed message queues, as well as to experts in real-time analytics and cloud solution developers seeking to gain a deeper understanding of the architectural schemes and methods for optimizing throughput applied in Kafka, Kinesis and RabbitMQ. In addition, the research results may be useful to scientists in the field of distributed computing and the Internet of Things, focusing on the theoretical foundations and practical aspects of constructing reliable event-data pipelines.

This paper analyzes automatic phase selection devices (APSD) used to ensure reliable power supply for single-phase consumers in three-phase electrical networks. Existing devices are classified into digital and analog types, and their schematic structures, operating principles, and application areas are examined. The results show that while digital APSDs provide higher accuracy and adaptability, they have a more complex structure and higher cost. Analog devices are simpler and cheaper but cannot effectively mitigate voltage unbalance in modern grids. Currently, due to the large-scale integration of single-phase photovoltaic (PV) inverters into the grid, the issue of voltage unbalance has become more pressing. Therefore, this paper proposes the development of a new generation of APSDs capable of calculating the unbalance factor, automatically selecting the optimal phase, and performing real-time monitoring.

This article presents a comprehensive analysis of artificial intelligence and its impact on various aspects of sustainable development. AI is actively utilized to enhance decision-making, automate processes, and optimize numerous fields of activity. However, its integration into critical domains such as sustainable development, public administration, and cultural innovation raises concerns regarding accessibility, inclusivity, and resource redistribution. The study examines key aspects of artificial intelligence, its classification—including narrow and general AI—and the technologies employed, such as machine learning, natural language processing, and computer vision. Special attention is given to AI’s relationship with sustainable development goals and its role in advancing innovative solutions in social and environmental spheres. The article also explores the ethical, social, and cultural consequences of AI implementation, emphasizing the necessity of developing responsible, transparent, and sustainable systems that align with international standards and ensure long-term societal well-being. This research may be of interest to a broad audience of specialists and researchers engaged in fields related to AI development, its applications, and its influence on societal processes.

This paper examines the paradigm shifts in leveraging generative artificial intelligence for automated code generation at the enterprise level. It is thus a critical review of prevailing prescriptions for integrating LLM agents into the software development lifecycles of modern enterprises, assessing their impact on team productivity and the new risks they introduce to confidentiality and licensing matters. The study would therefore be most befitting at this stage, as fast-forward steps are being made towards organizational adoption of generative AI, from mere IDE autocompletion features to more than a co-programmer but an autonomous agent capable even of popping pull requests sans humans in the loop, demanding new forms of legibility both organizationally and technically. The novelty of this research lies in its integration of material from scholarly works, industry reports, and case studies, along with lab pilot runs of Copilot and actual DevSecOps implementations, to triangulate the current state and future promise of this technology on a practical business level. Key findings include: a reduction of development cycle time by 50–60% without compromising code quality thanks to the integration of AI agents into IDEs and CI/CD pipelines; a shift of developers’ roles toward architects and reviewers as routine tasks are delegated to digital co‑programmers; and a necessity for phased implementation that accounts for private code protection and compliance with licensing norms. Significant barriers identified include model hallucination management, ensuring the traceability of changes, and adapting organizational culture and regulations to new roles such as prompt designers and AI-agent curators. The article will be of use to IT department heads, software architects, DevSecOps specialists, and researchers in the field of artificial intelligence.

Accurate prediction of stock market crashes is a longstanding challenge in financial analysis, with significant implications for investors, regulators, and policymakers. This paper presents a comprehensive review of existing machine learning (ML) approaches used in the prediction of financial crises and market crashes. It evaluates a wide range of techniques—including supervised learning, unsupervised learning, ensemble models, and deep learning—highlighting their strengths, limitations, and performance in various market conditions. Key challenges such as data imbalance, feature selection, temporal dependencies, and the interpretability of predictions are discussed. Building on the insights from existing literature, the paper proposes a novel hybrid framework that integrates multi-source financial indicators, sentiment analysis, and time-series modeling to enhance crash prediction accuracy and reliability. The study contributes to the growing field of AI-driven financial forecasting and provides a foundation for future research on robust early warning systems.