Volume 7.
Issue 09.

The article presents a theoretical and applied analysis of the methodological foundations for merging structured and unstructured data sources within machine learning systems. The study is based on an interdisciplinary approach that integrates architectural design of ML pipelines, data representation theory, and practices of heterogeneous format integration. Particular attention is paid to the analysis of recent scientific publications highlighting the application of Retrieve–Merge–Predict architectures, agent-based discovery systems, and multimodal frameworks involving large language models. Four stable strategies for data merging are identified, ranging from static unification to end-to-end processing within a unified training loop. The importance of selecting matching metrics and adaptation schemes when dealing with unstable data streams is demonstrated using experiments from Cappuzzo and Eltabakh. Special emphasis is placed on the methodological limitations of universal solutions, including the generalization paradox, sensitivity to structural evolution, and the lack of formalized testing scenarios in agent-oriented pipelines. It is shown that sustainable development of ML architectures requires a shift from linear ETL pipelines to coherent, iterative systems with internal adaptation and feedback from the model to the data. The article will be of interest to researchers in data preparation automation, developers of multimodal ML systems, data engineering specialists, and digital platform architects working with multi-format sources.

The convergence of lean manufacturing philosophies and predictive maintenance enabled by Industry 4.0 technologies represents a transformative paradigm for contemporary industrial systems. While lean manufacturing has historically focused on waste elimination, flow optimization, and continuous improvement, predictive maintenance leverages machine learning, digital twins, and advanced data analytics to anticipate equipment failures and enhance asset reliability. Despite extensive scholarly work on lean implementation frameworks and a rapidly expanding body of literature on predictive maintenance, the integration of these two domains remains theoretically fragmented and empirically underexplored. This research develops a comprehensive, publication-ready synthesis that conceptually and analytically integrates lean manufacturing principles with predictive maintenance architectures within Industry 4.0 environments. Drawing strictly on established literature, the study elaborates how organizational culture, change management, digitalization, and data-driven decision-making jointly influence operational efficiency, sustainability, and competitive advantage, particularly in small and medium-sized enterprises and process industries. A qualitative, theory-building methodology is adopted, involving systematic interpretive analysis of peer-reviewed frameworks, comparative models, and conceptual architectures. The findings reveal that predictive maintenance functions not merely as a technological enhancement but as a strategic enabler of lean objectives such as waste reduction, variability minimization, and value stream stability. Furthermore, the analysis demonstrates that successful integration depends on socio-technical alignment, interpretability of machine learning models, and organizational readiness for continuous learning. The discussion critically evaluates limitations inherent in current frameworks, including scalability challenges, data quality constraints, and cultural resistance, while proposing future research trajectories focused on hybrid lean–digital maturity models. The study contributes to both theory and practice by offering a unified conceptual foundation that advances understanding of how lean manufacturing and predictive maintenance can be synergistically operationalized in the era of Industry 4.0.

This paper presents the design, simulation, and characterization of a dual-band microstrip patch antenna intended for sub-6 GHz wireless communication applications. The proposed antenna incorporates a rectangular patch with optimized slots and a partial ground plane to achieve dual-band operation while maintaining a compact form factor. Detailed parametric studies were conducted to fine-tune the resonant frequencies and impedance bandwidths. The antenna was fabricated on an FR4 substrate, and its performance was evaluated using both simulation and experimental measurements. Results demonstrate that the antenna operates effectively across two frequency bands—centered at 3.5 GHz and 5.2 GHz—with satisfactory return loss, radiation patterns, and gain characteristics suitable for modern wireless systems such as 5G and WLAN. The findings highlight the potential of the proposed design for integration into portable and low-profile wireless communication devices.

This article examines the role of artificial intelligence in managing vulnerabilities across data infrastructures. It describes the methods used to identify, analyse and remediate weaknesses, as well as the difficulties encountered during deployment. The purpose of the study is to evaluate how AI is applied to data-security tasks and to assess its capabilities and limitations. A review of academic publications, risk-management models and publicly available information on cyber-attacks provides a broad perspective on the topic. Algorithms for monitoring network activity, forecasting threats and automating vulnerability remediation are discussed. Findings show that AI accelerates remediation processes by handling large data volumes and adapting to shifts in the threat landscape. Persistent challenges include data-quality issues, ethical risks and the possibility that the technology could be misused for illegal purposes. The need for robust, transparent models that resist manipulation is underscored. The material will benefit cybersecurity professionals, AI developers, IT managers and researchers who focus on the ethical aspects of new technologies.

Artificial intelligence (AI)-based predictive analytics has also become a revolutionary approach to contemporary financial planning, as it allows organizations to harness the power of large volumes of data to make accurate predictions, risk management and strategic planning. This article reviews how the element of predictive analytics is being incorporated into corporate financial systems and their potential to contribute to more accurate decisions, more effective utilization of resources and creating sustainable competitive advantages. With a quantitative comparative methodology, the authors review secondary data published in industry reports, financial databases, and peer-reviewed case studies, extending the discussion to use cases in bank, asset management, and corporate treasury activities. Evidence suggests that companies that implement AI-driven predictive analytics enjoy an average improvement in forecasting accuracy, an average 15-20 percent reduction in operational expenses, and a 10-15 percent increase in ROI within the first two years of use. The paper introduces a dual-frame model: the algorithmic basis and strategic refractions of AI implementation into financial workflows and a KPI-based ROI measurement framework based on practical performance experience. The innovation is in the combination of technical information on AI modeling with applied strategic financial performance, which addresses a gap noted in previous literature where implementation has not always been explained in terms of concrete business performance. This research makes a contribution to the financial technology literature by linking predictive analytics outcomes to executive-level decision pipelines, giving the research a dual theoretical and practical value to CFOs, risk managers, and policymakers. The paper closes with findings and recommendations on how to implement effective governance, ethical compliance and capability development strategies to ensure long term value contribution through adoption of AI in the context of an increasingly data-driven economy.

The construction sector is a major consumer of raw materials and a significant generator of waste. Transitioning from linear to circular material flows in construction requires integrated approaches encompassing material science, waste processing technologies, procurement strategies, and policy and organizational mechanisms. This paper synthesizes evidence from technical studies on recycling and reuse of construction materials, case studies on deconstruction practices, life-cycle perspectives, material flow analysis, and procurement and management literature to present a unified theoretical and practical framework for circular construction. The manuscript critically examines recycled concrete and masonry aggregates, alternative thermal insulation from textile wastes, wood–plastic composites with secondary materials, and systemic reuse of building elements. It further explores the role of procurement and project delivery methods in enabling circular outcomes, identifying barriers, drivers and stakeholder interactions. Methodologically, the research integrates a rigorous literature synthesis, comparative conceptual modelling, and a reasoned normative framework that maps technical potential to procurement strategies and organizational processes. Results show that while recycled aggregates and alternative insulation materials can meet many technical performance parameters, their adoption is conditioned by quality assurance mechanisms, regulatory clarity, and procurement incentives. Sustainable procurement models — particularly collaborative and integrated procurement practices — significantly improve reuse outcomes by aligning incentives, fostering trust and enabling whole-life cost assessments. The discussion elaborates on theoretical implications for industrial ecology in construction, critiques existing lifecycle assessment approaches, and offers detailed recommendations for policy, practice and research. Limitations include reliance on secondary literature and heterogeneity in empirical studies; future research should deploy multi-scalar empirical trials combining material testing with procurement pilots. The paper concludes with a comprehensive, actionable roadmap to accelerate circular construction that bridges technical feasibility and organizational adoption through targeted policy, procurement reform, and cross-sectoral collaboration.

Background: In the containerized architecture on Fargate design, business logic resides within the API repository, and an infrastructure repository contains a description of the infrastructure. Since startups must iterate rapidly and deploy new versions frequently, a fast and reliable CI/CD pipeline is critical, regardless of the chosen container platform. The regular solutions are either expensive and slow ones (such as Terraform Cloud, Atlantis, or Spacelift), or even if you have a self-hosted plan, or even with a self‑hosted Terraform pipeline, running a full plan/apply for every deployment is slow and adds unacceptable latency to releases in an MVP or startup context between the Terraform state and the actual cloud configuration.
Methods: A typical process utilizing the track_latest property, which was added in February 2024 — specifically, version 5.37.0 of the Terraform AWS Provider. Concurrently, the Terraform configuration invokes data.aws_ecs_container_definition with track_latest = true, so that a subsequent Terraform plan compares not with the ARN stored in the state file, but with the latest revision in the cloud.
Results: Across a sample of 50 releases, the complete cycle was reduced from 9.6 ± 1.1 minutes to 1.9 ± 0.2 minutes—an approximately 80 % acceleration. Once track_latest was enabled, all subsequent Terraform plan executions in the three environments completed with no changes. Infrastructure is up‑to‑date, eliminating drift.
Conclusions: Enabling the track_latest attribute in the Terraform AWS Provider enables a lightweight, secure, and deployment of ECS services without the need for external CI tools or workaround scripts. A single configuration parameter supplants expensive and complex solutions, preserving Terraform’s declarative paradigm and automatically preserving Terraform’s declarative paradigm and preventing drift—Terraform plan compares against the live revision. At the same time, the state file itself retains the prior ARN. The method’s limitations are the requirement for a provider version ≥ v5.37.0 and for tracking environment variable changes made outside of Terraform.

The new aspect of integrating blockchain technology in business finance has turned out to be a game-changer in solving challenges that have long plagued transparency, business operations efficiency, and stakeholder trust. This paper presents the measurable effect of blockchain integration inside financial J-managers by integrating the latest empirical research findings of multinational corporations, financial organizations, and fintech developers. Multimethod, with a mix of quantitative and analytical elements, will be used to evaluate the improvement in performance in terms of transaction processing time, cost per transaction, fraud incidence rates, audit cycle duration, and trust perception index. High-quality data obtained in verified industry reports and peer-reviewed research indicate that the use of blockchain can reduce settlement time by up to 60 percent, transaction costs by an average of 30-50 percent, and increase the rate of fraud detection by about 50 percent in comparison to legacy systems. Further, stakeholder trust rates, which are independently calculated in a survey improve significantly after a blockchain adoption due to the nature of the immutable verifiable ledger. What makes this research novel is its holistic criticism of operational efficiency and trust indicators in a single analytical framework, which fills in the gap in the literature that tends to discuss these aspects independently. The results indicate that blockchain can be used to improve financial process performance, strengthen compliance by ensuring auditability, and in general, increase the level of trust between counterparties. The final recommendation section provides several suggestions on what policymakers, regulators, and financial leaders can do to make sure that they are getting the most out of blockchain and that they are also managing to overcome any barriers that it may face during its adoption, including regulatory uncertainty, interoperability issues and scalability problems. This study gives a blueprint of how a blockchain can be implemented within business finance to produce real and sustainable enhances in the transparency-efficiency-trust triangle.

Background: The rapid advancement of Large Language Models (LLMs) has fundamentally altered the cybersecurity landscape, shifting the paradigm from technical exploitation to cognitive manipulation. Malicious actors now leverage generative AI to automate high-precision social engineering attacks, utilizing Open Source Intelligence (OSINT) to craft hyper-personalized spear-phishing campaigns at scale. Methods: This study analyzes the intersection of personality psychology—specifically the Big Five personality traits—and generative AI capabilities. We examine the theoretical framework of LLM-powered attacks, where attackers predict victim personality traits from digital footprints to tailor psychological triggers. We further evaluate the efficacy of counter-measures rooted in User Behavior Analytics (UBA) and AI-driven instantaneous response systems. Results: Analysis suggests that traditional signature-based detection systems are insufficient against LLM-generated content which lacks typical phishing indicators. However, defense mechanisms that integrate personality-aware baselines and behavioral anomaly detection demonstrate a higher potential for identifying synthetic social engineering attempts. Conclusion: We propose a "Cognitive Firewall" framework that combines psychological resilience training with automated, AI-driven behavioral monitoring. As LLMs lower the barrier for sophisticated attacks, defensive strategies must evolve to protect the human layer through proactive, context-aware algorithmic intervention.

Financial statement fraud is a persistent challenge that undermines investor trust, corporate governance, and financial market stability. Traditional auditing approaches often fail to capture subtle manipulations within complex financial data, highlighting the need for advanced computational methods. In this study, we investigate the effectiveness of machine learning models in detecting fraudulent financial reporting. Using a publicly available dataset, we applied rigorous preprocessing, feature selection, and feature extraction techniques before evaluating five models: Logistic Regression, Support Vector Machines, Random Forest, Gradient Boosting Machines, and Deep Neural Networks. The results indicate that Gradient Boosting Machines achieved the best overall performance, with an accuracy of 94%, precision of 91%, recall of 88%, and an AUC-ROC score of 0.96. Random Forest also demonstrated strong performance, particularly in balancing recall and F1-score. These findings suggest that ensemble-based models are highly effective for identifying complex fraud patterns in financial statements. The study provides empirical evidence supporting the integration of machine learning into auditing and financial risk management systems, offering a scalable and reliable approach to strengthen fraud detection practices.

The increasing pace and complexity of cyberattacks have made cybersecurity a more than technical aspect, even to the extent that cybersecurity is now a part of corporate financial strategy. This paper will address the relationship between business resilience and cyber risk exposure with specific reference to investment decision making in IT security, on quantifiable financial outcome. Using a mixed approach, the study combines secondary quantitative data--based on validated industry reports, stock market response studies, and corporate financial disclosures with qualitative analysis of resilience strategies across a variety of sectors. The return on investment (ROI) of proactive security spending is evaluated by regression modeling and scenarios of financial simulation of direct (e.g., breach response, legal liabilities) and indirect costs (e.g., reputational damage, market valuation decline). The analysis shows that companies that invest a greater proportion of their annual revenues in cybersecurity experience statistically significant decreases in the financial losses they incur in the event of a breach, and faster time to resume normal operations, which again translates into greater investor confidence in the company and a higher credit rating. In addition, cyber resilience can be incorporated into enterprise risk management frameworks to help organizations ensure greater alignment of capital allocation to longer-term value creation. The uniqueness of the study is that it helps connect the gap between cyber risk modeling and corporate finance because the study presents the issue of cybersecurity as a strategic asset and not a discretionary cost. These findings offer practical recommendations to Chief Financial Officers (CFOs), Chief Information Security Officers (CISOs), policymakers, and investors, specifically, the need to co-locate IT security spending with overall business resilience and financial management planning.

Background: High-precision sensing is vital for modern technology, yet conventional sensors are fundamentally constrained by classical noise limits. Quantum sensing leverages principles of quantum mechanics, such as superposition and entanglement, to overcome these barriers, offering unprecedented sensitivity. This review comprehensively analyzes the current state of quantum sensor technology, focusing on its underlying physical principles, emerging applications, and the pathway toward industrial maturation.
Methods: We synthesize findings from key research areas, detailing the foundational quantum systems like Nitrogen-Vacancy (NV) centers in diamond and advanced optical techniques. The paper reviews the theoretical methods used to achieve sub-shot-noise performance and discusses the system-level engineering challenges necessary for practical deployment, particularly concerning microwave control and optical readout. We also assess the current landscape of standardization efforts for quantum technologies.
Results: Significant breakthroughs are highlighted in both biomedical and environmental sensing. Applications range from tracking cellular redox-status using electron-paramagnetic resonance to ultra-sensitive salinity measurements. The analysis confirms that quantum sensing provides superior resolution compared to classical counterparts, unlocking new capabilities in in-vivo and real-time monitoring.
Discussion: While offering transformative potential, widespread adoption faces hurdles, including the maintenance of quantum coherence in ambient environments and the high cost and complexity of current instrumentation. Future efforts must focus on miniaturization, integration with classical systems, and standardized fabrication protocols. Quantum sensing represents the next-generation technological paradigm for metrology.

Cloud computing has rapidly established itself as the prevailing model for enterprise IT, with major providers such as Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP) leading global adoption. The cloud promises scalability, flexibility, and cost efficiency, but it also creates complex governance, risk, and compliance challenges due to shared infrastructure, multi-tenancy, and interdependent service layers. To guide assurance efforts, ISACA has issued dedicated audit frameworks: the AWS Audit Program (2019), the Azure Audit Program (2020), the GCP Audit Program (2023), and a broader Cloud Computing Audit Program (2016). These programs structure risk assessment and testing across domains such as governance, identity and access management, incident response, configuration management, logging, and business continuity.
To integrate these audit practices with enterprise-level governance, the study employs the COBIT 2019 framework, ISACA’s globally recognized model for governing and managing information and technology. COBIT 2019 provides structured objectives and processes across governance, planning, implementation, service delivery, and monitoring that link IT controls directly to business goals, risk optimization, and value delivery.
This study undertakes a comparative review of the cloud audit programs, aligning their focus areas with COBIT 2019’s governance and management objectives. The findings highlight distinct emphases: AWS concentrates on configuration and misconfiguration risks, Azure underscores continuity, shared responsibility, and service reliability, GCP emphasizes hierarchical structure, identity, and permission inheritance, and the general cloud computing program provides a broad governance foundation applicable across providers. Comparative analysis shows Azure exhibits the closest alignment with COBIT 2019, while AWS and GCP reveal gaps in governance integration. To address these gaps, the study proposes harmonization strategies involving cyber-risk quantification, structured risk registers, and continuous auditing. By linking technical audit domains to COBIT 2019’s governance objectives, the study reframes cloud audits from static, checklist-based exercises into dynamic governance mechanisms that foster compliance, risk optimization, and digital trust.

This study analyzes the issue of high energy consumption in industrial pneumatic transportation systems (PTS), which constitute an integral part of numerous technological processes. The objective of the research is to systematize and evaluate existing methodologies for optimizing compressed air supply in order to reduce operating costs and increase the overall energy efficiency of PTS. To this end, a review of specialized scientific publications over the past five years was conducted, devoted to the modeling, control and modernization of pneumatic transportation systems. The methodology is based on a comprehensive approach: theoretical foundations of two-phase flows are examined, the effectiveness of adaptive control systems (PID controllers, PLC, MPC) is compared, and the performance of modern compressor and peripheral equipment is evaluated. Based on the obtained data, a practical step-by-step framework for small and medium-sized enterprises is proposed, including an audit of the current state, digital modeling and implementation of optimization solutions. Within the study, specific recommendations are formulated regarding the selection of components and the configuration of operating modes, which allow achieving a reduction in specific energy consumption without significant capital expenditures. It is concluded that the integration of adaptive control algorithms and the precise selection of equipment constitute key factors in the pursuit of maximum energy efficiency. The information presented in this work will be of interest to engineers, production managers and researchers in the field of industrial energy efficiency.

The article discusses the development and justification of a Zero-Touch framework for multi-level authentication, which provides a dynamic balance between user convenience and security reliability when performing operations of varying risk levels. The relevance of the study is determined by the need to minimize user friction without reducing the level of protection, which requires new models of adaptive authentication. The paper aims to develop and methodologically substantiate a Zero-Touch framework that automatically strengthens authentication checks only when risk increases, relying on session context (behavioral, network, and hardware parameters) and the regulatory requirements of NIST SP 800-63B, PSD2, and GDPR. This approach eliminates unnecessary steps for low-risk operations and ensures a reliable escalation process for critical actions. The novelty of the proposed approach lies in the integration of four asynchronous layers (risk assessment engine, Policy Decision Point, user journey orchestrator, and log analytics) with a three-level risk gradation, aligned with AAL1–AAL3. The innovative architecture ensures a seamless user experience, invisible blocking of suspicious requests, and selective strengthening of factors for only a fraction of operations, which fundamentally differs from the static schemes of traditional MFA. Results of piloting the Zero-Touch framework were a jump in authentication accuracy to 86% with only 12% false positives, a System Usability Scale rating well above 80 points, plus five percentage points added to critical transaction conversion, and reduction of incident response time to minutes while maintaining validation delays at 5–7 seconds even when it has to be escalated. This article is intended for researchers and developers of information security systems, digital service architects, and compliance specialists.

This paper discusses the practical and feature gaps that were encountered with Google Document AI in building the AI product at TrialBase platform (ai.trialbase.com), which automates legal document analysis. Results matter because there is an explosion of electronic legal documents that require fast and reliable parsing, which is essential for systems based on LLMs and retrieval-augmented generation. Standard Document AIs seldom work well in practice, even if there are no damaged PDFs, and if a large dataset is being used, wherein the API quota is not hit, and processing costs do not matter.  The architecture proposed in this paper is robust, efficient at transforming various documents into structured data. Event-driven microservice architecture with message queues and a PDF sanitization pipeline solves real-world problems by enabling ProcessorPool (multiple processors using synchronous Document AI API to go beyond quota limitation concurrently drastically reducing processing times). Pre-sanitization, coupled with asynchronous batch processing and a custom load balancer, got a tenfold speed increase with enhanced reliability over real-world legal documents. The article is meant to help LegalTech researchers and practitioners, workflow developers, and engineers working on high-performance, reliable Google Cloud-based projects.

This paper discusses the use of a microservice architecture with Spring Boot and Spring Cloud for scaling banking applications. It aims to identify the constraints of conventional monolithic banking platforms and then design architectural solutions with Spring Boot that offer modularity, separate scaling capabilities, and ease of service testing. The motivation for such a study lies in explaining how transaction volumes are growing very fast in the global payments industry and further increases are forecasted through instant transfers, together with highly stringent regulatory requirements under PSD2, GDPR, and Basel III Besides this, downtime in banking systems is highly costly-with the possibility of running into several thousand dollars per minute. The novelty of the research lies in the comprehensive combination of five key Spring Cloud components—Service Discovery, API Gateway, Config Server, Circuit Breaker and Observability—with the Database per Service pattern, the saga pattern and two‑phase commit, CQRS and Event Sourcing; this approach ensures high fault tolerance, regulatory compliance and predictable horizontal scaling under peak loads. The main conclusions indicate that the proposed microservice framework reduces downtime to industry-minimum levels, simplifies adaptation to changing regulatory requirements, guarantees data integrity and flexibility, and that built-in security mechanisms and Zero-Trust models secure personal and critical operational data. This article will be helpful to IT architects and developers in banking organizations and fintech companies who are engaged in designing and implementing scalable, fault-tolerant microservice systems.

The accelerating convergence of advanced data analytics, artificial intelligence, regulatory governance, and digital infrastructures is fundamentally reshaping both financial and healthcare systems worldwide. Across emerging and developed economies alike, institutions are under growing pressure to enhance operational efficiency, improve stakeholder engagement, ensure regulatory compliance, and promote inclusive, ethical, and sustainable service delivery. This study develops a comprehensive, multidisciplinary analytical framework that integrates customer relationship management systems in healthcare, predictive and behavioral analytics in financial risk management, fair lending and regulatory compliance mechanisms, digital banking infrastructures, and intelligent computational architectures such as hybrid AI models, AutoML, neuromorphic computing, and distributed data systems. Drawing strictly on recent peer-reviewed literature, this research synthesizes theoretical, conceptual, and applied perspectives to demonstrate how data-driven governance can serve as a unifying foundation for improved decision-making across sectors. Using a qualitative, integrative research methodology, the article analyzes patterns, complementarities, and tensions across domains, highlighting how intelligent systems enhance patient engagement, credit risk assessment, housing finance, environmental policy enforcement, and operational resilience. The findings reveal that while technological innovation significantly improves transparency, accuracy, and scalability, it simultaneously introduces ethical, regulatory, and socio-economic challenges that demand robust governance structures. The discussion advances nuanced interpretations of institutional readiness, data ethics, and long-term sustainability, offering a forward-looking research agenda. This study contributes to theory by bridging healthcare informatics, financial analytics, and intelligent computing, and to practice by outlining actionable pathways for institutions seeking holistic digital transformation.

This article explores the integration of artificial intelligence (AI) technologies into information security systems, aiming to enhance the effectiveness of threat detection and response. The research is grounded in a comprehensive review of existing literature. It examines AI’s capabilities in processing large volumes of data, forecasting potential threats, and automating their identification. The discussion also addresses associated vulnerabilities, including issues related to the quality of training datasets, susceptibility to adversarial attacks, and algorithmic bias. Special attention is given to the development of technological solutions designed to protect AI-driven systems themselves. These include data encryption, access control mechanisms, anomaly detection systems, behavioral analytics, and architectural strategies such as multi-layered defense and containerization. The findings presented are relevant to cybersecurity researchers and practitioners, machine learning specialists, and developers of intelligent systems engaged in building interdisciplinary approaches to threat analysis, prediction, and mitigation in today’s complex digital environments. This material is also of value to professionals seeking to bridge theoretical frameworks with practical implementation in the context of rapidly evolving digital ecosystems and critical infrastructure.

The advent of digital transformation, facilitated by state-of-the-art IT systems, is changing the strategic paradigm of corporate finance by empowering organizations to attain increased efficiency, transparency, and nimbleness in the financial decision-making process. This article discusses how IT can serve as a strategic tool in generating quantifiable business value in the context of corporate finance departments, in particular its impact as a driver of capital allocation and risk management, forecast accuracy and value creation. The research employs a quantitative comparative study design synthesizing the secondary data found in the financial databases, industry benchmarks, and peer-reviewed case studies, and applications in the corporate treasury, capital budgeting, and financial reporting. Increased financial forecasting precision, operational cost reductions, and ROIC growth by 25-35%, 15-20%, and 12-18 percent, respectively, are indicators of success demonstrated by empirical evidence in the first three years of adoption by the organizations that implement IT-enabled digital transformation initiatives in the financial realm. The analysis also notes that integrated enterprise system, advanced analytics platform, and automation technologies can be used to increase working capital efficiency, ensure better regulatory compliance, and enhance stakeholder trust. The novelty in this research is entered by providing the linkage between how new technology may be adopted and how quantifiable corporate finance performance results are achieved. The research has developed a performance measurement framework that ties IT investment to the tangible and intangible business value. By integrating technical knowledge of IT systems with the strategic mindsets of finance, the study will serve the research domain of digital transformation with a feasible guide that the CFOs, finance strategists, and policymakers can consider. The paper ends with practical suggestions on governance, capability building and performance monitoring so that the digital transformation of finance can deliver sustained competitive advantage in an ever more technology-based economy.

The insurance industry stands at an inflection point where traditional actuarial approaches — based on limited historical data and human judgment — are being overwhelmingly supplemented or replaced by data‑driven, automated risk‑assessment systems. This article synthesizes emerging developments from both industry and academic research to outline a comprehensive framework for how predictive analytics, Internet of Things (IoT) data streams, and machine learning algorithms together can transform underwriting, pricing, and claims management in Property & Casualty (P&C), health, and life insurance lines. Drawing on recent empirical and conceptual studies, the paper describes how real‑time sensor data and historical records can be fused to create dynamic risk profiles; how claims processing may be accelerated and fraud mitigated using AI; and how insurers can realize cost efficiencies and competitive differentiation. The paper discusses methodological considerations, operational challenges (data quality, privacy, governance), and strategic change‑management imperatives. With this integrated paradigm, insurers can shift from reactive “detect and repair” models to proactive “predict and prevent” risk management — paving the way for more accurate pricing, personalized policies, enhanced customer satisfaction and sustainable profitability.

Cardiovascular disease (CVD) is a leading cause of morbidity and mortality worldwide, and early detection is critical for improving patient outcomes. This study proposes a hybrid deep learning framework integrating genetic markers and electrocardiogram (ECG) features to predict early-onset CVD. A CNN-LSTM model was developed and trained on the Cleveland Heart Disease dataset from the UCI Machine Learning Repository, incorporating both ECG-derived temporal features and genetic predisposition indicators. The model achieved an accuracy of 92.5%, precision of 91.2%, recall of 90.8%, F1-score of 91.0%, and an AUC-ROC of 0.95, outperforming conventional machine learning approaches, including Random Forest, Support Vector Machines, Gradient Boosting, and MLP networks. Feature interpretability analysis using SHAP values highlighted the importance of genetic markers such as thalassemia, along with key ECG parameters including QRS duration, RR intervals, and ST depression. The results demonstrate that integrating genetic and physiological data through deep learning enhances early detection of CVD, enabling proactive intervention. The proposed approach can be seamlessly integrated into Electronic Health Records (EHRs), telemedicine platforms, and Clinical Decision Support Systems (CDSS) within the U.S. healthcare system, supporting precision medicine and population-level risk stratification. This study underscores the potential of AI-driven predictive models in transforming cardiovascular healthcare by providing personalized, timely, and accurate risk assessments.

This paper explores how FinTech innovations and IT infrastructure can support financial inclusion and improve digital payment systems. Using recent global data - such as India Unified Payments Interface (UPI) which recorded 19.47billion transactions worth 25.08trillion (~USD 293 billion) in July 2025 and the World Bank Findex metrics which show account ownership in developing economies increased in 2022 (71%) compared to 2011 (42%). This paper uses a data-driven mixed-methods design. Quantitative analysis makes use of cross-country time-series data by regressing IT infrastructure readiness (including mobile broadband access indicators, digital public infrastructure measures) on digital payment adoption and financial inclusion indicators. The qualitative insights are based on the case studies of India Stack (including UPI) in India and Africa, which is fast growing in its digital payment ecosystem. Based on key findings, there was a statistically significant positive correlation between the quality of IT infrastructure and the magnitude of digital payment adoption- particularly in ecosystems with digital public infrastructures such as the UPI. This synergy has been able to catalyze quantifiable increases in account penetration and transaction volumes especially to underserved populations. The originality of the study is that all of the technological infrastructure, empirical modeling, and business ramifications were combined in order to shed light on the ways of sustainable financial inclusion. Potential implications of the findings to FinTech firms include strategic application of platform-based models to operate with robust infrastructure, whereas, suggestions to policy-makers include investment in open and interoperable systems and the promotion of regulatory frameworks to support scalable and inclusive digital financial ecosystems.

The article examines key features of interior design for coworking spaces and next-generation offices. Based on a systematic literature review, three interrelated groups of design solutions have been identified: spatial flexibility with adaptive layout strategies; comfort zones employing biophilic design; and the integration of smart technologies alongside digital-detox areas. The study demonstrates that the application of modular furniture and movable partitions enables rapid transformation of space to suit diverse work scenarios. The incorporation of biophilic elements and restorative zones is shown to reduce stress and enhance cognitive performance, while the combination of IoT infrastructure with acoustic pods and analog retreats balances productivity with mental recovery. The potential of an interdisciplinary model is described, uniting location factors, post-digital and post-work “comfort territories,” as well as the aesthetic dimensions of “post-touristic” décor. The material presented will interest researchers in architecture and hybrid workspace design—including ergonomics and neuropsychology experts analyzing the influence of spatial configurations on cognitive productivity and interpersonal interaction within collaborative environments. Furthermore, it will appeal to project leaders developing corporate and coworking platforms, urban planners, and strategic real-estate consultants seeking to assess the economic, social, and environmental effectiveness of innovative office solutions.

Under conditions of high price volatility in commodity markets, tightening environmental regulation, and chronic overcapacity, enterprises of the mining and metallurgical complex face the imperative not of incremental but of structural shifts in operational efficiency. In response to this challenge, the paper develops and theoretically substantiates an integrative framework that combines project management (PM) with lean manufacturing practices as complementary mechanisms of organizational development. The central proposition of the study is that sustainable operational excellence is achieved not by isolated Lean initiatives, but by their systematization and disciplined implementation within the contour of mature project procedures: from portfolio prioritization and stage-gate management to benefits control and the replication of best practices. Methodologically, the work draws on a systematic analysis of academic and industry literature, content analysis of managerial documentation, and case studies. In conclusion, it is argued that the synergy of PM and Lean, reinforced by digital transformation (sensors and MES/APS, analytics and predictive models, process mining), moves the enterprise from point improvements to systemic, reproducible changes in the operating model, with a direct effect on financial results and business resilience. The materials contained in the study will be of interest to senior executives and operations directors of the mining and metallurgical complex, as well as to researchers in industrial engineering and strategic management.

Legacy mainframe systems continue to serve as the technological backbone of global financial institutions due to their reliability, scalability, and transaction-processing capabilities. However, the increasing demand for cloud-native architectures, digital banking services, and operational agility has accelerated the need for modernizing COBOL-based banking applications. Traditional modernization approaches are often labor-intensive, costly, and susceptible to errors because of undocumented business rules, tightly coupled dependencies, and decades of accumulated technical debt. This paper proposes an Autonomous AI Agent Framework for COBOL-to-Cloud Banking System Modernization that integrates artificial intelligence-driven software analysis with automated migration assistance. The proposed framework employs specialized AI agents to perform source code understanding, dependency discovery, business rule extraction, data flow analysis, documentation generation, migration planning, and validation of transformed components. The framework further incorporates large language model-assisted code interpretation with static analysis techniques to improve maintainability while preserving business logic integrity. A conceptual evaluation based on representative banking modernization scenarios demonstrates the framework's potential to reduce manual effort, improve migration accuracy, accelerate documentation generation, and minimize modernization risks. The study highlights the practical applicability of autonomous AI agents in enterprise legacy transformation and provides a structured roadmap for financial institutions planning gradual migration from COBOL-based environments to cloud-native architectures.

This paper provides a systematic approach to planning and operating multisite network environments in the face of economic, natural, and cyber crises. The goal of the study is to identify methodological approaches and technological solutions that ensure maintaining business processes via a geo‑distributed network and synthesize them. This includes RTO and RPO requirements and topologies (active–active, active–passive, geo‑distributed clusters) definition as well as a hybrid‑cloud scenario economic feasibility assessment. The relevance of this work is grounded in the increasing sensitivity of the digital economy to downtime, rising cyber risks, and socio-economic shocks (such as migration waves and climate catastrophes), which demand strategic responses to multidimensional threats based on distributed infrastructure and international standards. The novelty of the research lies in a comprehensive content analysis of 18 sources—from reports by Uptime Intelligence, IBM, and UNHCR to the ISO 22301, DORA, NIST SP 800‑34, and ISO/IEC 27001 regulations. A cascading approach to calculating RTO/RPO involves criticality classification of services, dependency modeling, regular drill-over tests, and the development of financial justifications for CAPEX and OPEX flows under TCO and risk bonus. Key findings show that with multisite architecture plus a hybrid cloud, resilience to failures is ensured by automatic failover, regular DRP/BCP testing in operation monitoring MTTR/MTBF continuous error‑budget management; international standards integration resulting in exhaustive checklists for Business Impact Analysis and verification of target metrics; SOAR automation application plus predictive maintenance which mitigates staff shortage problem speeding up recovery; network segmentation and multilayer encryption inside Zero‑Trust framework. This article will be of use to IT infrastructure managers, network system architects, and specialists in business continuity and information security.

Enterprise Resource Planning (ERP) systems have evolved from mere transaction management tools to strategic platforms that enhance organizational adaptability and decision-making. However, their effectiveness is increasingly tested in environments characterized by geodynamic instability—particularly coastal regions that face frequent disruptions from erosion, seismic activity, and climate-induced flooding. This study examines the intersection of ERP system functionality and organizational resilience within such volatile geophysical contexts. Drawing on case analyses from coastal enterprises and municipal infrastructure bodies, the research investigates how integrated data flows, predictive analytics, and cloud-based continuity modules enable faster recovery and real-time risk mitigation. The findings suggest that organizations deploying adaptive ERP architectures—incorporating geospatial intelligence, supply-chain redundancy, and multi-tier contingency frameworks—demonstrate significantly higher operational continuity following geodynamic events. The study proposes a resilience-oriented ERP model that aligns technological infrastructure with environmental risk profiles, offering a blueprint for sustainable enterprise governance in vulnerable coastal economies.