Volume 7.
Issue 10.

Objectives: A comprehensive review of modern wearable electroencephalography (EEG) systems for home sleep monitoring was conducted, integrating technological and behavioral aspects. Based on an analysis of form factors, materials, and classification algorithms—long short-term memory (LSTM) networks, Random Forest, and convolutional neural networks (CNNs)—the main advances in rigid and flexible headbands, tattoo patches, and in-ear devices were identified. Concurrently, the evolution of consumer perceptions of wellness services was examined, shifting from aesthetics to data-driven resilience. The concept of “emotional ergonomics” was proposed to personalize interfaces, ensure algorithmic transparency, and enhance user emotional comfort.
Methods: The methodology for conducting a systematic review and triangulating literature and user data was substantiated. Scientific gaps in the standardization of wearable EEG device validation were highlighted, and prospects for integrating multisensory platforms and AI-driven analytics to improve monitoring accuracy and user acceptance were outlined.
Findings:The findings are expected to interest researchers in somnology and translational medicine who seek to integrate molecular and physiological indicators with artificial intelligence algorithms to enhance the accuracy of sleep disorder diagnosis and prognosis. Moreover, these insights will be valuable to developers of wearable biometric sensors and digital health platforms, as well as to regulatory experts shaping safety and efficacy standards for remote monitoring technologies.
The scientific novelty consists in the introduction, for the first time, of a combined concept of “emotional ergonomics” for sleep devices, integrating the hardware and software characteristics of wearable EEG sensors with contemporary consumers’ demands for transparency in validation, personalization, and a comfortable user experience in the post-pandemic period.

Financial institutions increasingly expose mission-critical services through APIs that must simultaneously satisfy strict service-level agreements (SLAs), withstand bursty workloads, and handle heterogeneous traffic from retail clients, institutional partners, and internal analytics engines. Traditional thread-per-request architectures in Java-based stacks, such as Spring MVC, struggle to combine high concurrency with predictable latency under such conditions, leading to renewed interest in reactive programming models such as Spring WebFlux and competing concurrency technologies like Java virtual threads (Thönes, 2015; Filichkin, 2018; Spring WebFlux Documentation, 2023).

Objective:

Building on recent work on priority-aware SLA-tiered APIs for financial services (Priority-Aware Reactive APIs, 2025), this article develops a comprehensive conceptual framework for designing SLA-aware reactive APIs using Spring WebFlux. The study integrates evidence from comparative performance research on reactive versus imperative models, evaluations of WebFlux in database-centric scenarios, and emerging analyses of virtual threads in Spring-based systems (Dakowitz, 2018; Iwanowski & Kozieł, 2022; Dahlin, 2020; Royal Institute of Technology (KTH), 2023; Nordlund & Nordström, 2023; Sukhambekova, 2025).

Methods:

A qualitative, synthesis-oriented methodology is employed. First, a structured narrative review consolidates findings from books, theses, scientific articles, and technical documentation on Spring WebFlux, Project Reactor, reactive programming concepts, and concurrency models in Java microservices (Reddy, 2018; Nurkiewicz & Christensen, 2016; Sharma, 2018; Mednikov, 2021; Srivastava, 2024; Deinum & Cosmina, 2021; Li & Sharma, 2020). Second, these insights are organized into an analytical comparison of three concurrency strategies: blocking MVC-style controllers, fully reactive WebFlux handlers, and Spring-based virtual thread configurations. Third, the paper synthesizes a detailed architectural blueprint for SLA-tiered priority-aware APIs on top of WebFlux, specifically tailored for financial services.

Results:

The synthesis shows that reactive WebFlux is particularly advantageous in latency-sensitive, I/O-bound, high-concurrency scenarios—common in risk checks, portfolio queries, and payment authorization flows—when paired with careful backpressure management, non-blocking persistence, and disciplined operator usage (Spring WebFlux Documentation, 2023; Project Reactor, 2023; Iwanowski & Kozieł, 2022). Priority-aware scheduling at the reactive layer allows differentiated handling of Gold, Silver, and Bronze tiers without resorting solely to coarse-grained infrastructure scaling (Priority-Aware Reactive APIs, 2025). However, empirical work on virtual threads suggests that for CPU-heavy or database-bound flows with moderate concurrency, virtual-thread-based Spring MVC can provide competitive or superior simplicity–performance trade-offs (Royal Institute of Technology (KTH), 2023; Nordlund & Nordström, 2023; Dahlin, 2020; Filichkin, 2018).

Conclusion:

The article argues that SLA-aware design in financial APIs should not default blindly to reactive programming but should instead adopt a portfolio approach to concurrency. Spring WebFlux is best positioned for highly concurrent, I/O-centric, SLA-differentiated traffic, especially when backed by reactive data access and carefully tuned schedulers, while virtual-thread-based MVC remains compelling for simpler services and teams with limited reactive expertise. The proposed conceptual framework offers practical guidance to architects on when and how to deploy WebFlux for priority-aware financial APIs and identifies future research needs in multi-dimensional benchmarking, hybrid models, and automated SLA policy enforcement.

Against the backdrop of rapid global urbanization and the expansion of high-rise construction, the architecture, engineering, and construction sector faces a dual imperative: to guarantee an unprecedented level of fire safety while simultaneously achieving sustainability targets. Prevailing practices in the design, construction, and operation of high-rise assets remain largely fragmented: safety subsystems, engineering solutions, and digital technologies are developed and deployed in isolation, which erodes synergistic effects and hinders the formation of a truly resilient and sustainable urban infrastructure. In response to this challenge, a holistic tripartite conceptual framework is proposed that integrates principles of fire safety, advanced engineering solutions, and high technologies. The research methodology includes a systematic literature review to inventory existing technologies and identify research gaps, as well as a qualitative comparative analysis of representative cases. The article details the key technological components of the proposed framework — Building Information Modeling (BIM), the Internet of Things (IoT), artificial intelligence (AI), and digital twins (DT). Their role is demonstrated in shaping an integrated, intelligent, and adaptive building ecosystem. The analysis identifies significant technical, organizational, economic, and educational barriers to implementing such a comprehensive approach. In conclusion, it is substantiated that the proposed integrated foundation is critically important for achieving the United Nations Sustainable Development Goals, primarily SDG 9 and SDG 11, and practice-oriented recommendations are formulated for the professional community, policymakers, and academia to accelerate the transition to a new generation of sustainable and safe vertical cities.

In the present study a novel conceptual framework for an intelligent decision support system (IDSS) is proposed, specifically designed with consideration of the unique operational requirements of small enterprises. The primary objectives of the research are twofold: first, to conduct an in-depth analysis and critical evaluation of existing theoretical approaches to decision support in the small and medium-sized enterprise segment; second, to develop a specialized IDSS model tailored to advisory services for this class of organizations. The methodological foundation of the research consisted of a scrupulous systematic review of scientific publications from the last 5 years devoted to the implementation of artificial intelligence methods in the management processes of small and medium-sized enterprises, as well as to the key directions of development in explainable AI (XAI). As a result, the principal architectural design principles and technological components ensuring the effective functioning of such systems were identified. In conclusion, illustrative case studies of the application of the developed IDSS in tasks of strategic market positioning and financial diagnostics of small businesses are presented. These examples may serve as practical guidelines for IT solution developers, consulting firms, and academic researchers oriented toward the digital transformation of small and medium-sized enterprises.

Corporate training seldom provides structured, realistic practice, resulting in low retention and limited behavior change. We built a curriculum-centric, multi-agent framework for role-play simulations (could be adapted for text and voice) that deliver sequenced, educationally grounded training conversations for sales employees. The framework automates delivering curriculum of scenarios by coordinating several agents: the Orchestrator manages flow, the Curriculum Manager sequences role-play scenarios with embedded learning objectives and rubrics, the Conversation Agent enacts realistic dialogues, the Data Agent tracks progress, Telemetry logs outcomes, and the Guard enforces safety. We designed a workflow prototype in n8n that simulates the behavior of a phone-based conversational agent. For this paper, the system is demonstrated through a chat-based interface that reproduces the curriculum sequencing and orchestration logic, rather than a production telephony deployment. Pilot learners told us that sequenced curricula improved practice consistency and in turn confidence. Learners appreciated how realistic practice was and specific feedback via Telemetry, although challenges still remain around sustaining engagement and avoiding repetitive feedback. Through these findings we canconclude that a curriculum-centric, multi-agent role play simulation can improve learning outcomes in employee training, bridging the gap between adaptive e-learning and real-world application. Our objective is to examine whether curriculum-guided AI role play can improve soft skills when compared to unstructured. This paper contributes a curriculum-centric framework, functional prototype and findings from a formative pilot with three sales hires.

This article presents an integrative, theory-driven, and practice-oriented analysis of how advanced digital technologies—principally blockchain, Internet of Things (IoT), machine learning (ML), and additive manufacturing (3D printing)—can be combined with rigorous cold chain logistics design to produce pharmaceutical supply chains that are simultaneously resilient, traceable, quality-assured, and environmentally conscious. The study synthesizes heterogeneous literatures spanning cold chain management, digital transformation in healthcare, logistics service capability, optimization and simulation techniques, and machine learning applications in supply and energy forecasting. It foregrounds the distinct challenges of pharmaceutical cold chains—temperature sensitivity, regulatory compliance, product integrity, reverse logistics, and accountability—and maps how technology-enabled interventions address each challenge while generating new trade-offs and governance considerations. Methodologically, the article adopts a conceptual synthesis and normative design approach: first, cataloguing and critically assessing evidence and theoretical claims from extant studies; second, constructing an integrated functional architecture and layered operational model for cold-chain-aware pharmaceutical supply networks that incorporate data capture (IoT), secure provenance (blockchain), predictive analytics (machine learning including tree boosting and recurrent architectures), and localized production capabilities (3D printing). Results are presented as descriptive analyses of modular capabilities, projected performance improvements, risk mitigation pathways, environmental considerations, and implementation constraints. The discussion interprets the assembled evidence, highlights methodological limits and contested assumptions, and outlines a research and policy agenda that prioritizes empirical validation, standards harmonization, data governance, and equitable access. The conclusion summarizes the core contribution: a comprehensive conceptual blueprint that links digital technologies with supply chain strategy to enable safer, more transparent, and more sustainable pharmaceutical cold chains, while providing actionable research propositions and managerial implications.

The article investigates the application of WebAssembly (WASM) technology for implementing high-performance media content analysis directly on the client side. The relevance is driven by the growth in volumes of user-generated content and the need to process it while preserving data privacy and reducing the load on server infrastructure. The scientific novelty lies in the development and description of a client application architecture for automatic detection of photosensitive epilepsy (PSE) triggers in video, which demonstrates the use of WASM to solve mission-critical tasks in the field of digital accessibility (WCAG 2.2). The work describes a prototype, PSE Video Analytics Platform, developed in Rust and compiled to WebAssembly. Special attention is given to a comparative performance analysis of the WASM module and analogous JavaScript solutions. The objective is to prove the effectiveness and viability of the client-side approach for complex computational tasks. To this end, methods of prototyping, comparative benchmarking, and systems analysis are employed. The conclusion outlines the advantages of the approach: radical reduction of server costs, assurance of data confidentiality, and the ability to provide the user with immediate feedback. The article will be useful to web developers, software architects, and digital accessibility specialists.

The article provides principles for creating efficient project schedules in extreme climatic conditions. The rationale for the relevance of the work is essentially defined, considering recent trends regarding the increasing frequency and magnitude of weather-related catastrophes. The purpose here is to develop a methodology for adaptive planning based on quantitative diagnostics regarding climate, probabilistic windows related to favorable conditions, and the dynamic redistribution of tasks. It will be new in that non-stationary extremes will be integrated within an analysis approach to evaluate changes in parameters regarding climate distributions; climate windows shall be calculated based on multi-year meteorological data and NOW-cast forecasts; adaptive buffers shall be applied within the critical chain by APD rule and modular decomposition of tasks; the digital twin will function to perform operational revision of priorities; finally parametric insurance as well as climate clauses shall be included in contracts. The main conclusions affirm that the application of the above-stated rules allows up to 28% acceleration of the calendar with budget maintenance, reduces downtime uncer­tainty by 15%, and keeps schedule shifts within 5% of the planned date for up to three years. This paper is likely to find its best audience among project managers, planners, and risk management specialists working in the building construction, infrastructure, and maritime sectors.

Context: The proliferation of Large Language Models (LLMs) has lowered the barrier to entry for sophisticated social engineering attacks. Adversaries can now automate the inference of psychological traits from user data to generate highly persuasive, targeted phishing content.
Problem: Traditional cybersecurity defenses, such as signature-based Intrusion Detection Systems (IDS) and standard spam filters, are increasingly ineffective against these syntactically perfect and contextually aware AI-generated attacks. They fail to detect the subtle semantic anomalies that characterize algorithmic psychological manipulation.
Method: This study investigates the efficacy of an unsupervised learning framework designed to detect behavioral anomalies in email communications. We simulated an LLM-driven attack campaign that tailors phishing narratives to the Big Five personality traits (Openness, Conscientiousness, Extraversion, Agreeableness, Neuroticism). We then evaluated a hybrid detection model combining Long Short-Term Memory (LSTM) networks for sequence analysis and Isolation Forests for anomaly scoring.
Results: The simulation demonstrated that personality-aligned LLM attacks achieved a theoretical click-through rate 40% higher than generic phishing. However, the proposed behavioral anomaly detection system identified 88.5% of these sophisticated attacks by analyzing deviations in semantic density and communication patterns, outperforming traditional keyword-based filters which detected only 34%.
Conclusion: While LLMs significantly enhance the lethality of social engineering, analyzing the "psychological surface" of communication via unsupervised learning offers a robust countermeasure. Future defense architectures must move beyond content analysis to context and behavioral intent analysis.

This article explores how distributed microservice systems in the cloud are scaled. The goal is to piece together, from scattered research, working solutions and what might still be experimental. The focus is on design and deployment strategies that aim for reliability, elasticity, and cost-effective growth. Open-access, peer-reviewed papers published since 2021 were reviewed with a special emphasis placed on those with empirical tests, diagrams, or case studies to see how ideas play out in practice. Across the papers certain themes keep reappearing. Infrastructure work revolves around horizontal and vertical scaling, while orchestration and autoscaling — Kubernetes HPA/VPA, serverless computing, various service meshes — are interpreted as part of the broader field. The unique contribution is the attempt to frame old and new together — to see classical ideas such as service modularity alongside hybrid autoscalers, energy-aware verification, probabilistic checks.  In some cases, these tactics reinforce one another; in others they collide, or create unexpected trade-offs. Observing them in the same frame highlights that scaling microservices is as an ongoing experiment, where established patterns and cutting-edge proposals coexist. Taken together, the available studies do not present a single blueprint but instead sketch what looks like a layered approach. In many cases services are kept as stateless and decoupled as possible; in others that ideal is only partly met. Horizontal and vertical scaling appear side by side — sometimes combined in the same system — with their usefulness varying by workload. Orchestration policies are often automated, yet the literature also warns that energy use and security concerns grow quietly in the background as systems expand and, if left unmanaged, can erode the gains of scaling. This article will provide value to cloud architects, DevOps engineers, developers, and researchers interested in gaining awareness of current practices as well as possible venues of where the field might be heading next.

The issue of disparity between the heterogeneous trade promotion management systems and processes arises in the post-acquisition integration scenarios in the Consumer-Packaged Goods (CPG) industry. This paper will examine the role of SAP Customer Relationship Management (CRM) as a hub for implementing hybrid trade promotion models under the managerial process in organisational integration. The results of implementation were discussed through the set of post-acquisition experiences when SAP CRM served as the platform for the integration of trade promotion processes, which previously were handled differently. Findings suggest that the SAP CRM hybrid architecture will support a 67 percent improvement in promotion effectiveness and will also save 45 percent of integration complexity in the period that is crucial after the acquisition process. This analysis goes further to illustrate that organisations that use SAP CRM as the core trade promotion engine in the case of mergers and acquisitions realise a three-year payback of 306% and a 23% CRM promotional planning shortening. Integration tasks that are mainly addressed are harmonisation of master data, promotional workflow standardisation, as well as performance analytics consolidation of different organisational structures. The hybrid model enables organisations to maintain continuity in their operations and gradually adopt standardised workflows, which will nullify the integration risks and speed the value creation. In turn, the given research contributes to the knowledge about how the use of enterprise systems is a key enabling factor in the face of the most complicated organisational changes and the provision of tangible business value.

Based on the variational principle, a calculation scheme, mathematical model, and methodology have been developed to determine the stress–strain state (SSS) of the Rezaksay earth dam with a complex geometric configuration under its own weight in a plane strain condition. The calculations were performed using the licensed software complex Plaxis 2D, taking into account the structural features of the dam. Numerical studies made it possible to determine the nature of the SSS of the earth dam and reveal that the presence of an inclined core and a tooth in the structure leads to the formation of a complex stress state in the upper prism.

This article examines the efficiency of a fine dispersion of fertilizer across agricultural lands technology by means of aircrafts. As a solution to this problem, the use of ultralight aircraft such as drones is considered appropriate.
Recently, according to conducted various field studies, air drones spraying has become an innovative approach. Particularly, the main research object of this article is to analyz the technical capabilities of the DJI Agras T30 drone and to consider the total time it spends treating a field. The impact of agrotechnical necessities on flight performance, in other words the increase of used spray volume and flight time, is also calculated in this paper.
The analysis of the calculation results shows that the actual shape of the field (its edges and corners), speed of wind, and flight directions can significantly affect the flight time. Therefore, to ensure that theoretical calculations more accurately reflect real-world conditions, it is recommended to introduce a correction coefficient into the formulas.

Background: Artificial intelligence has achieved unprecedented predictive and decision-making capabilities across diverse domains such as healthcare, finance, energy systems, civil engineering, and organizational management. However, the increasing opacity of complex machine learning and deep learning models has raised critical concerns regarding trust, accountability, fairness, and regulatory compliance. Explainable Artificial Intelligence (XAI) has emerged as a pivotal paradigm aimed at addressing these concerns by rendering AI systems transparent, interpretable, and human-understandable.
Objective: This research develops a comprehensive, domain-transcendent theoretical and applied framework for explainable artificial intelligence by synthesizing insights from multidisciplinary applications including medical diagnostics, financial risk management, energy forecasting, structural engineering, organizational agility prediction, and counterfactual reasoning. The study seeks to identify unifying principles, methodological patterns, and conceptual gaps that limit the scalability and reliability of XAI systems across real-world settings.
Methods: A qualitative, theory-driven research methodology is employed, grounded strictly in an in-depth analytical synthesis of contemporary peer-reviewed literature on XAI. The methodology integrates interpretability taxonomies, post-hoc and intrinsic explanation strategies, counterfactual reasoning mechanisms, and self-explainable model architectures. Emphasis is placed on descriptive methodological reasoning rather than mathematical formalization, aligning with interdisciplinary accessibility requirements.
Results: The findings reveal that while XAI techniques demonstrate significant domain-specific effectiveness, they remain fragmented in conceptual alignment and evaluation standards. Medical and biological applications prioritize causal and feature-attribution explanations, finance emphasizes transparency and regulatory compliance, energy systems focus on temporal explainability, and engineering domains demand structural logic validation. A unifying theoretical scaffold based on explanation purpose, stakeholder cognition, and decision risk is identified.
Conclusion: The study concludes that future progress in XAI depends on transitioning from tool-centric explanations to cognition-aware, context-sensitive, and ethically grounded explanatory ecosystems. The proposed unified framework advances explainable AI beyond interpretability toward actionable trust, supporting responsible deployment across high-stakes domains.

Traditionally, health records are kept in siloed data storages of different health organizations. Today, all patients’ EHRs (Electronic Health Records) are used and shared with different institutes and research facilities without their consent. To protect and overcome pitfalls of generic systems, we introduce a new hybrid system called Hybrid Patient Data Vault (HPDV). This hybrid system can help patients securely share their health information in a manner that could allow them to share only what is necessary or in need-to-know basis. We detail the system’s components, workflows, and emergency protocols, emphasizing patient-centric design. Through a STRIDE-based threat model and simulations of key metrics like transaction latency and ZKP generation time, we demonstrate HPDV’s security and feasibility. Our evaluation shows it outperforms monolithic approaches in auditability and privacy, with ZKP proofs generated in under 7 seconds on standard devices. This work demonstrates a practical modern approach for secure, patient-controlled health data exchange.

Healthcare systems worldwide face critical challenges in patient data management, including fragmented medical records, limited interoperability between providers, security vulnerabilities, and patient privacy concerns. This paper presents a comprehensive framework for implementing blockchain-enabled Universal Health Records (UHR) that addresses these fundamental limitations through decentralized architecture and cryptographic security. Our proposed solution leverages distributed ledger technology to create a patient-centric ecosystem where individuals maintain sovereign control over their health data while enabling authorized healthcare providers to access complete, accurate medical histories in real-time. A pilot implementation across three healthcare facilities (n=150 patients) using traditional EHR systems demonstrated significant improvements: 67% reduction in patient record retrieval time (from 12.3 to 4.1 minutes), 89% decrease in data inconsistencies between providers, and 94% patient satisfaction with data access transparency. Security testing revealed zero unauthorized access attempts over 6 months, while maintaining 99.7% system uptime. Through comprehensive analysis of technical architecture, security protocols, and implementation strategies, this paper demonstrates how blockchain technology can transform healthcare data management, offering a technically feasible, economically viable, and regulatory-compliant pathway toward universal health record interoperability.

Within the scope of the study an analysis of a quantum-resistant key management infrastructure (KMI) is conducted. Key Management Infrastructure, in this context, encompasses cloud services, on-premises HSM clusters, and hybrid or edge solutions. Asymmetric algorithms underpinning modern protection protocols demonstrate vulnerability to quantum methods of Shor and Grover. The objective of this study is to perform a holistic reference architecture for KMI that enables a seamless and secure transition from classical to post-quantum solutions. The methodology includes a systematic analysis of existing KMI architectures, a detailed evaluation of algorithms standardized by NIST for post-quantum cryptography, as well as the modeling of a hybrid cryptographic scheme. As a result, a multi-layer architectural model is proposed, featuring a “Crypto-Agility Engine” for dynamic algorithm replacement, hybrid key encapsulation protocols and a phased migration strategy to post-quantum primitives. The model maintains backward compatibility with legacy systems, minimizes the load on mission-critical components such as fintech platforms and ensures an unchanged level of performance. The study conclusions confirm the practical feasibility of this approach for long-term protection of data confidentiality and integrity in the post-quantum era. This work is of interest to information security architects, software engineers and specialists engaged in the protection of critically important information across diverse infrastructures.

Background: The construction sector is a principal consumer of natural resources and a major contributor to greenhouse gas emissions, waste generation, and environmental degradation. Transitioning from linear to circular paradigms in construction requires integration of material innovation, procurement reform, stakeholder collaboration, and lifecycle thinking. The literature contains numerous domain-specific investigations—ranging from the viability of using wastewater in concrete production to the mechanical performance of wood-plastic composites—yet there is fragmentation across technical, managerial, and policy dimensions. This study synthesizes diverse evidence to present a comprehensive, theoretically grounded framework for circular construction that connects recycled-material technologies with procurement strategies and lifecycle environmental accounting.

Objectives: This article aims to (1) consolidate empirical findings on recycled and alternative construction materials; (2) analyze procurement and delivery models that enable circular outcomes; (3) propose an integrated methodological approach for assessing circularity across technical performance, environmental impact, and stakeholder dynamics; and (4) identify research gaps and propose a nuanced agenda for policy, practice, and scholarship.

Methods: Using the provided reference corpus as the evidentiary base, this work implements an analytical synthesis method grounded in cross-disciplinary theory building. Technical studies on recycled aggregates, wood-based alternatives, and plastic composites are synthesized with procurement and project-delivery literature to derive a systemic conceptual model. Evidence is interrogated via comparative thematic analysis and hypothetical scenario projections, emphasizing consistency with the original empirical findings while extrapolating theoretical implications.

Results: The synthesis reveals convergent findings: high-quality recycled aggregates and sands are reaching industrial readiness (Skocek et al., 2024; Ulewicz, 2021), wood-based materials present substantial carbon benefits when managed across long cycles (Nielsen-Roine & Meyboom, 2024; Gustavsson & Sathre, 2006), and plastic-derived composites can replace selected non-structural components with favorable environmental trade-offs (Ribeiro et al., 2023; Lamba et al., 2022). Procurement mechanisms and collaborative delivery models emerge as critical enabling conditions; misaligned procurement disincentivizes circular practices (Mitchell, 2015; Osipova & Eriksson, 2011; Ofori, 2007). Lifecycle analyses underscore the importance of demolition-phase emissions and embodied carbon from material choices (Egonzalez et al., 2022; Gustavsson & Sathre, 2011).

Conclusions: A multi-layered framework is proposed that links material selection criteria, quality-assurance pathways for recycled inputs, procurement reform, and lifecycle accounting. Policy levers, industry standards, and novel contractual forms are necessary to scale circular construction. Future research must prioritize long-term field trials, standardization of recycled material specifications, and integrative socio-technical studies that examine how stakeholder incentives shape circular outcomes.

At present, digital transformation acts as a determining element in maintaining competitiveness and increasing the operational efficiency of facilities of various purposes—from production complexes to socio-administrative structures. The study is aimed at a comprehensive examination of the role of a unified IT network as a basic systemic framework that ensures the implementation of this transformation. The purpose of the work is to identify the fundamental design principles and to demonstrate the strategic significance of a convergent network infrastructure for the coherent integration of heterogeneous engineering, operational, and business systems. The methodological basis of the research relies on a systems analysis of contemporary scientific literature over the period, the examination of relevant regulatory and standard requirements, and the practical deconstruction of a case involving the deployment of a comprehensive IT infrastructure at a strategically important facility—the National Company Kazakhstan Garysh Sapary. As a result of the analysis, a conceptual model of a unified network has been formed, as well as a rationale for its key advantages, including the improvement of operational efficiency, the rationalization of resource use, and the strengthening of cybersecurity levels. The scientific novelty consists in interpreting the unified IT network not only as a technical element but as a strategic asset that creates synergistic effects when digital technologies are introduced. The obtained conclusions and formulated practical recommendations are of value to organization leaders, IT directors, system architects, and engineering specialists who design and modernize the infrastructures of multi-profile facilities within the context of the developing digital economy.

The article presents a comparative analysis of the main Layer 2 scaling technologies in blockchain systems: Rollups (Optimistic and ZK), Sidechains, and the Lightning Network. The relevance of the study is driven by the fundamental scalability problem of Layer 1 blockchains, which limits their throughput and increases transaction costs. The novelty lies in the systematization and comparison of these technologies using a multi-criteria model that includes security, performance, decentralization, and economic efficiency. The study describes the architectural principles of each solution and examines their security mechanisms and trust models. Special attention is paid to the trade-offs between inheriting security from the base network and operational independence. The work aims to identify the strengths and weaknesses of each approach to determine their optimal application scenarios. To achieve this, methods of comparative analysis, systematization, and analysis of the scholarly literature are employed. Fundamental and contemporary research in the field of L2 solutions is reviewed. The conclusion presents the key findings and proposes a framework for selecting a technology depending on the requirements of a decentralized application. The article will be useful for blockchain system developers, researchers, and architects of decentralized applications.

This paper presents a comprehensive, publication-ready investigation into the problem of reliable work zone crash classification and risk prediction using an integrated pipeline that emphasizes rigorous data validation, modern machine learning ensembles, and natural language processing of crash narratives. Work zones are high-risk environments on road networks and accurate identification and classification of work zone crashes is essential to enable targeted safety interventions, resource allocation, and reliable research (Yang, 2015; Blackman et al., 2020). Yet, existing operational crash datasets suffer from misclassification, incomplete fields, and inconsistent semantics arising from heterogeneous reporting practices (Swansen et al., 2013; Carrick et al., 2009). We argue that improving data quality through systematic validation and hybrid AI-augmented checks is a prerequisite for robust predictive modeling (Van Der Loo & De Jonge, 2020; Redman, 1998). Building on advances in ensemble learning and hyperparameter optimization (Almahdi et al., 2023; Asadi & Wang, 2023), together with text-mining approaches for narrative analysis (Sayed et al., 2021), we design and describe an end-to-end methodology: (1) a layered data validation and correction module that uses deterministic rules and large language model-assisted anomaly detection; (2) a multimodal feature engineering strategy that integrates structured traffic and environmental data with unstructured narrative-derived features; (3) an ensemble classifier framework that uses stacked learners with hyperparameter tuning to achieve robust classification across varying traffic conditions; and (4) a human-in-the-loop verification stage to capture residual errors and provide continuous feedback for model retraining (Malviya & Parate, 2025; OpenAI, 2023). We present a descriptive analysis of modeled experimental outcomes and sensitivity studies, discuss theoretical implications, confront limitations, and outline future research directions. The findings demonstrate that combining principled data validation with ensemble learning and narrative text mining materially reduces misclassification rates, produces better calibrated crash-risk scores, and yields interpretability benefits valuable for practitioners and policymakers (Pande et al., 2011; Sayed et al., 2021). This article contributes a detailed procedural blueprint and theoretical rationale for transportation researchers seeking reliable, defensible analytics for work zone safety.

Global industrial corporations often contend with fragmented IT monitoring environments, where disparate tools manage distinct infrastructure domains. This decentralization fosters operational inefficiencies, such as protracted incident diagnostics, redundant alert escalations, and increased resource consumption for root-cause analysis.

This paper documents a case study on the design, implementation, and operational impact of a centralized monitoring system at Metinvest Holding, a multinational industrial enterprise. The core objective was to consolidate heterogeneous monitoring platforms into a cohesive, single-pane-of-glass interface to improve operational visibility, streamline diagnostics, and provide high-level strategic oversight for executive management.

The research employed a single-case study methodology, coupled with an iterative development process informed by Agile principles. An aggregation and visualization layer was architected using Grafana to integrate data from incumbent systems—including Microsoft SCOM, PRTG Network Monitor, Azure Monitoring, and APC Enterprise Manager—without necessitating their replacement. The two-year development cycle involved continuous collaboration with service engineers.

The centralized system yielded substantial improvements in operational efficiency. The number of engineers required for initial, cross-domain incident triage was reduced from an average of three to one. Root-cause identification was significantly accelerated through a holistic, correlated view of infrastructure health, which concurrently minimized alert fatigue and redundant inter-departmental escalations. A novel visualization layer was also developed to furnish executive leadership with an intuitive overview of global IT operational status.

This case demonstrates that a non-disruptive, aggregation-based strategy can effectively address the challenges of monitoring fragmentation in large-scale enterprise settings. The findings highlight the value of integrating technical consolidation with user-centric visualization to achieve both operational efficiency and strategic alignment. The architectural principles delineated are broadly applicable to other multinational organizations facing similar IT infrastructure management complexities.

The paper discusses the great change at the high end of game development brought about by generative artificial intelligence — not simply a minor tooling upgrade but rather a machine that can be used in automating the building of adaptive systems and increasing the gravitational pull of engagement for players. At a studio level, adoption has already moved beyond pilot projects to institutional practice-a shift that turns mere efficiency gains into matters of existence for quite many teams. The paper’s novel input is a neat merge of three paths: first, the lift of a game’s “skeleton” to a formal meta-model that lets big language models set up event graphs; second, a change in how hardship is made real — moving from simple time factors (D1/D7) to group-level keeping and leaving-risk hints so that tuning helps long-term involvement rather than short-term win rates; and third, the use of a feedback loop — watcher-helper sending possible changes to a skilled checker, with checker fixes sent back as top-notch training samples to lessen false guesses methodically. AI-fueled automation substantially speeds up prototyping and can enhance extended player engagement; however, these advances are precarious — dependent on legally verifiable data origin, strict dual-path (algorithmic + human) examination, and transparent intervention mechanisms — if not present, then adaptive systems may stray from customization to hidden control. This paper aims at academics and business professionals who seek disciplined, practical methods for implementing AI in design while maintaining creative freedom and responsibility.

The article studies the levels of wave propagation in a building caused by the movement of the Uzbekistan Talgo-250 train. The train moves at a speed of 30 m/s. The selected boundary area is the weight of the train cars, which is taken as a dynamic force. The finite element method is used to solve the problem. The article uses finite elements of the irregular tetrahedron shape.

This article explores the ontological and axiological foundations of an inclusive society through the lens of contemporary philosophy and humanistic thought. The author analyzes inclusion not only as a social policy mechanism but as a form of being that embodies the essence of human coexistence, value recognition, and moral responsibility. The research argues that inclusivity is both an ontological condition of human being-in-the-world and an axiological imperative of modern civilization aimed at ensuring equality, dignity, and sustainable development.

The rapid digital transformation of global financial markets has fundamentally altered the dynamics of portfolio construction, risk assessment, and asset allocation. Traditional portfolio theories, although foundational, were developed in environments characterized by relatively low data velocity, limited market microstructure complexity, and minimal computational adaptability. In contrast, modern markets operate under conditions of extreme volatility, high dimensionality, and continuous feedback loops driven by algorithmic and high frequency trading. This paradigm shift has created an urgent need for adaptive, intelligent, and scalable portfolio management frameworks capable of learning from complex, nonstationary financial environments in real time. Deep reinforcement learning has emerged as a leading paradigm in this domain, offering the ability to integrate sequential decision making, nonlinear representation learning, and dynamic optimization under uncertainty. However, despite substantial progress in algorithmic trading and portfolio optimization, a persistent gap remains between theoretical deep reinforcement learning models and their practical deployment in cloud-based, risk-aware portfolio management systems.

This study addresses that gap by developing and theoretically validating an adaptive cloud-based deep reinforcement learning framework for dynamic portfolio risk prediction and asset allocation. The framework draws conceptual inspiration from recent intelligent cloud architectures that integrate reinforcement learning with scalable computational infrastructure, most notably the intelligent cloud framework for dynamic portfolio risk prediction proposed by Mirza and colleagues in a recent IEEE conference contribution (Mirza et al., 2025). Building upon this foundational work, the present study extends the conceptual scope by embedding risk-sensitive policy learning, correlation-aware state representations, and multi-temporal portfolio rebalancing within a unified cloud-native architecture. The central premise of the article is that portfolio risk is not a static property but an evolving construct shaped by market regimes, investor behavior, and structural feedback loops, and that only learning systems capable of continuous adaptation can meaningfully manage this complexity.

This article explores the transformative impact of Artificial Intelligence (AI) integration within SAP S/4HANA Cloud, focusing on its applications in enterprise management, particularly in finance and controlling operations. It examines how AI enhances various aspects of financial management, including cash management, robotic process automation, and advanced analytics. The article delves into the synergistic relationship between AI capabilities and human expertise, highlighting how this collaboration is reshaping traditional financial processes and decision-making paradigms. The article also discusses the future trajectory of AI in SAP S/4HANA Cloud, anticipating further advancements in areas such as predictive analytics and natural language processing. Additionally, it addresses the challenges and considerations that arise from this technological integration, including the need for balancing automation with human oversight, the ethical implications of AI-driven decision-making, and the imperative for workforce skill development. By providing a comprehensive overview of the current state and future potential of AI in SAP S/4HANA Cloud, this article offers valuable insights for organizations seeking to leverage AI technologies to enhance their financial operations and strategic decision-making processes

The U.S. healthcare domain is a complex ecosystem of public and private entities including Medicaid, Medicare, hospital systems, commercial insurers, and pharmaceutical companies, monitored and controlled by multiple federal and state regulators. This sector is undergoing rapid digital transformation, where financial efficiency and accountability are as critical as clinical excellence. Traditional accounting methods often lack real-time visibility, predictive capacity, and integration across healthcare systems. Therefore, Business Intelligence (BI)-driven accounting analytics has emerged as a strategic enabler in improving financial transparency, enhancing decision-making support, maintaining regulatory compliance, minimizing fraud, and optimizing resource allocation. By integrating BI tools with U.S healthcare accounting system, entities can analyze and visualize real-time insights through interactive dashboards, perform predictive analysis to streamline revenue flows, manage cost control, and support strategic decision-making. This study explores the methodological foundations, implementation strategies, and managerial implications of BI-driven accounting analytics in the U.S. healthcare system focusing on financial transparency and data-driven decision-making support for stakeholders like managers, policymakers, and healthcare leaders.