In the era of cloud-native computing, achieving high scalability, resilience, and performance has become a fundamental requirement for modern application development. This paper presents a critical review of Spring Boot, a leading Java-based framework, and its role in elevating application performance within distributed and microservices-oriented architectures. The study examines Spring Boot’s core features—such as embedded servers, auto-configuration, actuator endpoints, and integration with containerization and orchestration tools like Docker and Kubernetes—that streamline deployment and operational efficiency. Furthermore, it evaluates performance optimization techniques, fault-tolerance mechanisms, and scalability patterns enabled by Spring Cloud and reactive programming models. Through comparative analysis and case-based discussion, the review highlights both the strengths and limitations of Spring Boot in building resilient, cloud-native systems. The findings underscore Spring Boot’s effectiveness in simplifying complex infrastructure concerns while ensuring agility, observability, and robustness in modern software ecosystems.

This article examines the transformative role of artificial intelligence in enterprise cybersecurity monitoring, addressing the fundamental challenges that traditional security operations centers face in managing the exponentially growing volume of security events across complex digital environments. The article explores how machine learning approaches for anomaly detection enable organizations to identify threats without explicit programming for each variant, while also addressing the critical problem of alert fatigue through intelligent prioritization and correlation mechanisms. The article analyzes emerging human-AI collaboration models that redefine security workflows and distribute cognitive load optimally between analysts and automated systems, emphasizing the importance of explainable AI for building appropriate trust. Finally, the article examines future directions toward autonomous security response, identifying current limitations and promising approaches for safe partial-automation while considering regulatory frameworks and adversarial adaptation. Throughout the analysis, the article demonstrates how AI integration represents not merely a technological evolution but a strategic necessity for maintaining viable security operations in an increasingly complex threat landscape.

This article traces the evolution of escape rooms through the case of the Titanic Escape Room, framed as a cyber-physical system in which architecture, firmware, and hardware solutions are subordinated to the task of creating an invisible fail-safe technological substrate for an immersive experience. The study is relevant because the Location-Based Entertainment (LBE) industry is developing rapidly, determining success not through isolated puzzles but by sustaining a cinematic, plausible, and uninterrupted narrative. The article demonstrates that an experience-centric model replaces puzzle-centric design in this context. Through the application of cyber-physical systems theory and HCI principles, combined with fault-tolerant design, this work is novel due to its thorough engineering analysis of the Titanic system. The article proposes a distributed three-tier architecture (COGS server, Raspberry Pi media servers, Arduino nodes). This approach justifies its choice, also justifying the selection of communication protocols and failover strategies. Also examined in detail is the article's analysis of three engineering solutions: adaptive Morse-code decoder, hardware printer redundancy, and hybrid PCM/MP3 audio playback. These are interpreted as manifestations of a unified strategy of engineering for time and reliability, aimed at minimizing latency and covertly eliminating faults, which are preconditions for preserving immersion. The main findings underscore that immersiveness in LBE systems is a reproducible outcome achieved by strict adherence to real-time principles, adaptive interface design, and multilayer redundancy. The article will be helpful to researchers of cyber-physical systems, entertainment-technology engineers, HCI specialists, and developers of immersive LBE projects.

Purpose: This critical review systematically examines the complex interplay between ultra-low-power (ULP) design methodologies and the corresponding verification challenges in modern integrated circuits. The imperative for pervasive, battery-operated devices (IoT, wearables) has pushed design-for-power techniques to their limits, simultaneously creating significant hurdles for ensuring functional correctness. Methodology: The paper first establishes the theoretical foundation of power dissipation in CMOS circuits, followed by a systematic survey of leading ULP design techniques, including dynamic voltage/frequency scaling, power gating, and multi-threshold CMOS. It then evaluates state-of-the-art power estimation and, crucially, formal and simulation-based methodologies necessary for verifying the functional integrity and power intent, as formalized by the Unified Power Format (UPF). Findings: Aggressive power reduction techniques, particularly power gating, fundamentally alter the circuit's state and timing characteristics, rendering traditional verification flows insufficient. Formal verification, specifically equivalence checking and property checking based on Satisfiability (SAT) and Binary Decision Diagrams (BDD), is increasingly indispensable for exhaustively validating power management logic and state retention mechanisms.

Originality: This review offers a holistic synthesis, bridging the gap between ULP design methodology and its formal verification requirements, providing a foundational resource for researchers and practitioners navigating the dual constraints of energy efficiency and functional integrity in next-generation VLSI.

This article explores neuroarchitecture, as it represents an interdisciplinary approach to designing educational spaces that integrates advances in neuroscience, cognitive psychology, and architectural practice. Addressing of two contemporary challenges is what conditions the study’s relevance: the rise of mental-health problems among all students and the mismatch between customary school environments as well as the pedagogical demands of the twenty-first century. This study aims to identify and systematize principles of neuroarchitecture that enhance engagement, mitigate stress, and support cognitive productivity. The article is novel since it synthesizes theoretical foundations with evidence-based design solutions. After that, the article verifies them through the Shanti Elementary School project case study in the United States. The chief findings do indicate that biophilic designs and optimized light with color plus spatial flexibility not only restore attention but also reduce anxiety, and gain academic achievement, reduce absenteeism, plus heighten resilience among teaching staff. The paper emphasizes that the educational environment significantly influences both cognitive and emotional experiences, and that policy and practice should consider integrating neuroarchitectural strategies as a quality standard in education. Architects and educators in cognitive psychology and neuroscience, as well as researchers and academic administrators, will find the article to be useful.

The increasing sophistication of financial fraud in the U.S. banking system requires advanced and transparent detection mechanisms. This study proposes a blockchain-enabled machine learning framework that enhances fraud detection accuracy and data integrity. Using an open-source dataset from the UCI Machine Learning Repository, five supervised models—Logistic Regression, Decision Tree, Random Forest, Gradient Boosting, and Neural Network—were trained and evaluated. Data preprocessing included feature scaling, encoding, and class balancing to ensure reliability and model generalization. Results show that integrating blockchain’s immutable ledger with artificial intelligence significantly improves detection performance. The Neural Network model achieved the best results with 99.1% accuracy, 98.6% precision, 98.9% recall, and a 98.7% F1-score, outperforming all other algorithms. The blockchain layer ensured data transparency, traceability, and tamper resistance throughout the detection process. This research demonstrates that combining blockchain and AI can strengthen fraud prevention, enhance regulatory compliance under U.S. financial laws, and foster greater trust in digital banking operations. The proposed system offers a scalable and secure foundation for the next generation of fraud detection in the U.S. financial sector.

This article examines 60Out’s Godzilla vs. Kong escape room as a representative case of designing complex narrative–interactive environments in which spectacle, safety, and player engagement are fused into a single managerial system. The relevance of the study is defined by the contemporary evolution of the location-based entertainment industry, which is shifting from traditional game formats to comprehensive immersive spaces that must simultaneously deliver sensory impact, cognitive absorption, and operational reliability. The novelty lies in conceptualizing a model of coordinated orchestration, implemented through the COGS system, which functions as a practical instantiation of the Experience Manager from interactive narrative theory. This mechanism demonstrates the feasibility of integrating three traditionally separate domains—show control, the cognitive dynamics of flow, and engineering safety—into a unified, coherent control architecture. The principal findings indicate that the success of Godzilla vs. Kong derives not only from high-technology components, but from their systemic coordination: synchronization of multimedia to produce spectacle, nonlinear design and adaptive hints to sustain flow, and embedded monitoring and intervention capabilities to ensure safety. It is shown that such integration yields durable business outcomes (a 25% increase in bookings) and establishes the basis for a new disciplinary paradigm in which experience design is understood as the governance of interdependent parameters of risk, engagement, and dramaturgy. The article will be useful to researchers in interactive media, entertainment-industry professionals, and designers of themed environments.

The article proposes an original MLOps maturity model specifically oriented toward retail organizations. The relevance of the study stems from the fact that, despite active investment in machine learning, many retailers face difficulties with scaling, ensuring reliability, and assessing the return on investment of AI initiatives. The presented model serves as a roadmap for the phased and systematic development of MLOps practices. The scientific novelty lies in the domain adaptation of general MLOps principles to the retail context and, critically, in establishing clear and measurable criteria for transitions between five maturity levels. The paper analyzes existing universal maturity models. The five levels, from chaotic to optimized, are described through the lens of four key dimensions: technology and data, ML development, deployment and operations, governance and people. Particular emphasis is placed on the development of concrete checklists that make it possible to verify readiness to transition to the next level. The purpose of the study is to provide retail companies with a tool for self-assessment and strategic planning to build their MLOps capabilities. To achieve this goal, methods of analysis of existing models, synthesis, and domain adaptation are used. In conclusion, it is emphasized that a high level of MLOps maturity is primarily a strategic rather than a purely technical task. The material is addressed to CDOs, CIOs, heads of Data Science, and MLOps engineers in retail.

Purpose: This research addresses the persistent challenge of inefficient information handover and fragmented maintenance data management in complex port facilities by proposing an integrated digital framework. The study focuses on leveraging Building Information Modeling (BIM) for geometric context, the Construction Operations Building Information Exchange (COBie) standard for structured asset data, and an event-driven architecture for dynamic data processing. Design/Methodology/Approach: A design science approach was utilized to develop a novel three-tier framework. The methodology involved customizing the COBie schema to accommodate unique marine and mechanical port asset data, followed by architecting an event-driven mechanism based on microservices. An illustrative use case was employed to model the flow of inspection and sensor data, from event generation to the automatic update of structured COBie records, thus streamlining maintenance workflows. Findings: The developed framework successfully provides a structured, interoperable solution for port facility management. Key findings include a tailored COBie implementation guide for non-building port assets and the demonstration that the event-driven mechanism is associated with a significant reduction in the time-to-work-order initiation compared to conventional, manual processes. This approach transforms static BIM/COBie information into a living, dynamic data stream.

Originality/Value: This work pioneers the integration of an event-driven data processing methodology with BIM and the COBie standard specifically for the critical domain of large-scale port infrastructure maintenance, offering a pathway toward a fully realized Digital Twin of port assets.