International Journal of Advance Scientific Research
International Journal of Advance Scientific Research ISSN - 2750-1396 Last Submission:- 25th of Every Month Frequency: 12 Issues per Year (Monthly) https://portal.dnb.de/opac.htm https://zdb-katalog.de/title.xhtml?idn=1244864870
Integrating Large Language Models And Enterprise Automation: Architectural, Performance, And Process Implications For Modern Software Systems
The rapid integration of large language models into software development and enterprise automation has fundamentally reshaped how organizations design, build, refactor, and operate information systems. Tools such as AI-assisted code editors and intelligent automation platforms represent not merely incremental productivity aids but structural shifts in the relationship between human expertise, software architecture, and business process execution. This study investigates the convergence of large language model–driven development environments, enterprise software engineering practices, and automated business process management. Drawing strictly on established literature related to large language model engineering, refactoring theory, Java persistence performance, enterprise systems, robotic process automation, and workflow validation, this research develops a comprehensive analytical framework that connects artificial intelligence–augmented coding tools with enterprise-scale performance, reliability, and governance considerations. The methodology follows a qualitative, theory-driven synthesis of prior empirical and conceptual studies, emphasizing architectural patterns, performance trade-offs in CRUD-intensive systems, and the evolving role of automation across supply chains and forecasting functions. The results highlight how AI-enhanced development tools influence code quality, refactoring discipline, persistence-layer efficiency, and process automation reliability. The discussion critically examines limitations, including opacity in model behavior, risks of automation drift, and the challenges of validating complex AI-driven workflows. The article concludes by positioning large language models as a foundational yet incomplete component of future enterprise systems, requiring rigorous engineering, process alignment, and performance-aware integration to realize sustainable organizational value.
Copyright (c) 2026 Dr. Adrian Ionescu https://creativecommons.org/licenses/by/4.0
Integrating Building Information Modeling for Accurate Quantity Take-Off and Cost Estimation: Ontological, Processual, and Professional Perspectives
Building Information Modeling has progressively transformed the practices of quantity take-off and cost estimation in construction projects by introducing data-rich, model-based workflows that promise greater accuracy, transparency, and efficiency than traditional methods. Despite widespread adoption of BIM tools across design and construction phases, persistent challenges remain related to semantic consistency, modeling guidelines, professional roles, and the reliability of automated quantity extraction. This research develops a comprehensive and theoretically grounded examination of BIM-based quantity take-off and cost estimation by synthesizing insights from empirical case studies, ontological approaches, professional practice analyses, and methodological frameworks documented in the existing literature. Drawing strictly on prior academic and professional references, the study explores how BIM-enabled quantity take-off systems operate across tendering, design development, infrastructure, and facilities management contexts. Particular attention is given to the role of software platforms such as Autodesk Revit and Cubicost, ontology-driven algorithms for reinforced concrete quantities, and emerging integrations with sustainability and early-stage decision-making processes. The methodological approach of this research is qualitative and interpretive, relying on structured literature analysis to trace conceptual linkages, methodological evolution, and professional implications. Findings indicate that while BIM significantly reduces human-induced errors and enhances coordination between design and cost information, its effectiveness is contingent upon modeling discipline, semantic clarity, and the evolving competencies of quantity surveyors. The discussion highlights theoretical implications for construction informatics, identifies limitations inherent in current BIM-based quantity take-off practices, and outlines future research directions focusing on semantic healing, interdisciplinary collaboration, and lifecycle-oriented cost intelligence. The study concludes that BIM-based quantity take-off is not merely a technological shift but a paradigmatic transformation in how construction quantities, costs, and professional responsibilities are conceptualized and managed.
Copyright (c) 2025 Dr. Alejandro M. Ríos https://creativecommons.org/licenses/by/4.0
Global Construction Cost and Time Overruns in a Volatile Economic Landscape: Re-conceptualizing Project Performance, Risk Dynamics, and Control Mechanisms
The global construction industry occupies a pivotal position in national economies, serving as both a driver of infrastructure development and a barometer of macroeconomic stability. Despite its strategic importance, the industry continues to struggle with persistent cost and time overruns, a challenge that transcends geographic, economic, and sectoral boundaries. This research develops an integrated, theory-driven analysis of construction project cost and time overruns by synthesizing global economic outlooks, project management theory, contractor selection dynamics, and risk-based control mechanisms. Drawing strictly on established academic and institutional literature, the study situates construction overruns within broader global construction futures, economic volatility, and evolving interpretations of project success. Particular attention is paid to how macroeconomic shocks, such as financial crises and pandemics, interact with micro-level project decisions, including contractor selection strategies, working-hour dynamics, cost forecasting approaches, and control tools in megaprojects. Using an extensive qualitative and conceptual methodology, the article advances a holistic explanatory framework that links economic uncertainty, managerial practices, and institutional capacity to project performance outcomes. The findings emphasize that cost and time overruns are not merely technical failures but systemic manifestations of fragmented governance, misaligned incentives, and outdated success metrics. The discussion re-frames overruns as adaptive signals rather than anomalies, arguing for recalibrated definitions of project success that incorporate resilience, socio-economic impact, and long-term value creation. The study contributes to construction management scholarship by bridging macroeconomic foresight with project-level execution realities, offering theoretically grounded insights for policymakers, practitioners, and researchers seeking to enhance predictability and performance in an increasingly uncertain global construction environment.
Copyright (c) 2025 Dr. Alejandro Martín Rojas https://creativecommons.org/licenses/by/4.0
Microplastics and Nanoplastics as Multidimensional Environmental Stressors: Sources, Transport Pathways, Ecotoxicological Impacts, and Emerging Biodegradation Strategies
The exponential growth of plastic production over the past seven decades has resulted in the pervasive accumulation of plastic debris across marine, freshwater, terrestrial, and atmospheric environments. Among these pollutants, microplastics and nanoplastics have emerged as particularly complex and hazardous contaminants due to their small size, high surface-area-to-volume ratio, chemical persistence, and capacity to act as vectors for other environmental pollutants. This research article provides an integrated and theoretically expansive examination of microplastic and nanoplastic pollution, synthesizing evidence from marine, soil, atmospheric, and food web studies while critically analyzing their physicochemical properties, transport mechanisms, toxicological effects, and biodegradation pathways. Drawing strictly from the provided references, the article explores how microplastics originate from both primary and secondary sources, undergo fragmentation and weathering, and subsequently interact with biological systems at multiple trophic levels. Special emphasis is placed on their role as carriers of persistent organic pollutants, heavy metals, and microbial communities, thereby amplifying ecological and human health risks. Furthermore, the article critically evaluates current detection methodologies and remediation strategies, including microbial and genetically modified organism-based plastic degradation, with particular attention to polyethylene terephthalate and emerging biodegradable plastics. By elaborating theoretical frameworks, addressing counter-arguments, and identifying unresolved research gaps, this work contributes a comprehensive, publication-ready synthesis intended to inform future interdisciplinary research and policy development on plastic pollution management.
Copyright (c) 2025 Dr. Sofia Hernández López https://creativecommons.org/licenses/by/4.0
Integration of Thermal Comfort Modelling and Digital Technologies for Sustainable Circular Economy Practices in Built Environments
The convergence of thermal comfort research and digital technologies presents novel opportunities to enhance sustainability within built environments while simultaneously enabling circular economy practices. Thermal comfort has been extensively studied through both static and adaptive models, reflecting the complex interplay between human physiology, clothing behavior, environmental parameters, and occupant preferences (Fanger, 1970; De Dear & Brager, 1998; De Carli et al., 2007). Recent advancements in predictive maintenance, Internet of Things (IoT), and machine learning enable real-time monitoring and adaptive control strategies to optimize indoor environmental conditions, reduce energy consumption, and support sustainable practices across building operations (Farhan et al., 2015; Nayak, 2021). Furthermore, circular economy frameworks emphasize resource efficiency, product longevity, and digital integration to influence consumer behavior toward sustainable consumption (Chaudhuri et al., 2022; Charnley et al., 2022). This research synthesizes theoretical and applied knowledge from thermal comfort modeling, occupant behavior, and digital-enabled circular practices to propose a holistic framework for sustainable building management. Emphasis is placed on the interaction between occupant clothing adaptation models, digital monitoring technologies, and circular economy strategies, highlighting the potential to align human comfort with energy efficiency and resource conservation objectives. The findings suggest that combining advanced data-driven modeling of occupant thermal behavior with digital technologies can significantly reduce environmental impacts while supporting the transition toward circular operational models in buildings. This study addresses critical knowledge gaps by linking thermal comfort theory, predictive digital frameworks, and circular economy applications in a comprehensive analytical context.
Copyright (c) 2025 Arunesh Kumar https://creativecommons.org/licenses/by/4.0
Explainable and Ensemble-Based Machine Learning Frameworks for Credit Risk Assessment: Integrating Deep Learning, Alternative Data, and Interpretability in Modern Financial Decision-Making
Credit risk assessment has undergone a profound transformation over the past two decades, driven by the rapid advancement of machine learning, the increasing availability of alternative and behavioral data, and the growing regulatory demand for transparency and fairness in automated decision-making systems. Traditional credit scoring approaches, particularly logistic regression, have long been favored for their interpretability and regulatory acceptance, yet they often struggle to capture nonlinear patterns and complex borrower behaviors embedded in modern financial data. In contrast, advanced machine learning and deep learning models demonstrate superior predictive power but introduce significant challenges related to explainability, bias, and operational trust. This research develops a comprehensive conceptual and methodological synthesis of ensemble-based, deep learning, and explainable artificial intelligence (XAI) approaches to credit risk modeling, grounded strictly in recent and foundational academic literature. By integrating heterogeneous balancing strategies, profit-sensitive learning, behavioral and transactional data, and post-hoc interpretability techniques such as SHAP and LIME, the study articulates how predictive accuracy, fairness, and transparency can be jointly optimized. A descriptive methodological framework is proposed, emphasizing ensemble logistic regression, deep neural architectures, alternative data integration, and explainability layers. The findings suggest that hybrid and ensemble models, when augmented with robust XAI mechanisms, outperform single-model approaches not only in predictive reliability but also in regulatory compliance and stakeholder trust. The discussion critically examines trade-offs between profit maximization and equality, the ethical implications of alternative data usage, and the future role of explainable deep learning in credit risk management. This study contributes a unified theoretical perspective for financial institutions, regulators, and researchers seeking to balance innovation with accountability in credit decision systems.
Copyright (c) 2025 Dr. Aleksandar Petrovic https://creativecommons.org/licenses/by/4.0
Architecting Secure, Reliable, and Policy-Driven DevSecOps Pipelines for Java-Centric Cloud-Native and Hybrid Deployment Ecosystems
The accelerating adoption of cloud-native architectures, continuous delivery models, and hybrid deployment strategies has fundamentally transformed how Java-based enterprise systems are designed, released, and operated. While DevOps practices have successfully shortened development cycles and increased deployment frequency, they have also amplified systemic risks related to security vulnerabilities, policy non-compliance, operational instability, and cascading failures. This has driven the emergence of DevSecOps as a holistic paradigm that integrates security, reliability engineering, and governance into automated software delivery pipelines. This research presents a comprehensive, theoretically grounded investigation into secure, reliable, and policy-driven DevSecOps pipeline architectures for Java-centric systems operating across cloud-native, hybrid, and non-containerized environments. Drawing strictly on established literature and industry frameworks, the study synthesizes principles from static and dynamic application security testing, dependency vulnerability management, policy-as-code enforcement, reliability engineering, deployment strategies, and regulatory compliance. The methodology employs an integrative analytical approach, combining comparative evaluation of tooling ecosystems with conceptual modeling of pipeline stages and deployment patterns. Findings demonstrate that security and reliability are not competing objectives but mutually reinforcing outcomes when embedded early and continuously within delivery workflows. The study further reveals that policy automation and progressive deployment strategies significantly reduce operational risk while enabling organizational scalability. The discussion critically examines limitations related to tool integration complexity, cultural resistance, and evolving threat landscapes, while outlining future research directions for adaptive governance and intelligent pipeline orchestration. The article contributes a unified conceptual framework that advances academic discourse and provides actionable guidance for practitioners seeking to operationalize DevSecOps maturity in Java-based enterprise environments.
Copyright (c) 2025 Dr. Lucas A. Reinhardt https://creativecommons.org/licenses/by/4.0
Agile, Model-Based, and AI-Augmented Safety Assurance for ISO 26262–Compliant Automotive Systems: A Unified Theoretical and Engineering Perspective
Functional safety has become one of the most critical and complex challenges in modern automotive engineering due to the rapid evolution of software-intensive, autonomous, and intelligent vehicle systems. The ISO 26262 functional safety standard provides a comprehensive framework for managing risks associated with electrical and electronic automotive systems; however, its practical implementation faces increasing tension with agile development practices, model-based engineering, artificial intelligence integration, and the growing complexity of vehicle architectures. This research article develops a comprehensive, theory-driven, and practice-oriented analysis of contemporary approaches to ISO 26262 compliance, focusing on agile safety cases, model-based hazard analysis, simulation-driven verification, fault injection, safety mechanisms, and emerging AI-assisted safety engineering methods. Drawing strictly from the provided academic and industrial references, the study synthesizes established and emerging methodologies into a unified conceptual framework that reconciles rigor, traceability, and regulatory compliance with flexibility, scalability, and innovation. The article elaborates deeply on the theoretical foundations of safety cases, semantic relationships among engineering artifacts, automated verification pipelines, AUTOSAR-based safety validation, and ASIL-oriented hardware and software design. Special emphasis is placed on the integration of artificial intelligence into safety-critical systems, addressing the transition from traditional quality management to high-integrity ASIL-D compliance. Through extensive descriptive analysis, the article identifies key findings related to verification efficiency, safety argument robustness, and lifecycle sustainability, while also critically examining limitations, unresolved challenges, and future research directions. The study contributes an original, publication-ready synthesis that advances academic discourse and provides practical insights for researchers, safety engineers, and policymakers navigating the future of automotive functional safety.
Copyright (c) 2025 Dr. Michael J. Harrington https://creativecommons.org/licenses/by/4.0
Resilient Lockstep and Redundant Multicore Processor Architectures for Soft Error Mitigation in Safety-Critical Embedded Systems
The continuous scaling of semiconductor technologies has significantly increased the susceptibility of modern processors to transient faults, particularly soft errors induced by radiation effects such as single-event upsets and multiple-bit upsets. This challenge is especially critical in safety-critical embedded systems deployed in automotive, aerospace, industrial control, and high-reliability computing environments, where incorrect computation can lead to catastrophic consequences. Lockstep-based redundancy, dual-core and triple-core architectures, dynamic and heterogeneous replication techniques, and reconfigurable fault recovery mechanisms have therefore emerged as fundamental design strategies to ensure dependable operation. This article presents an extensive and original research-oriented analysis of lockstep and redundant multicore processor architectures for soft error mitigation, grounded strictly in the provided body of literature. Drawing from experimental heavy-ion irradiation studies, industrial processor implementations, FPGA-based softcore designs, and architectural modeling of fault trends, the paper elaborates on the theoretical foundations of fault tolerance, the evolution of lockstep techniques, and the practical design trade-offs between performance, area, power, and reliability. Methodological aspects are explored in detail, including architectural replication, error detection and comparison mechanisms, context reloading, dynamic lockstep activation, heterogeneous core approaches, and system-level monitoring using trace and debug infrastructures. The results are discussed in a descriptive and interpretive manner, emphasizing resilience improvements, detection coverage, and recovery behavior under realistic fault conditions. The discussion further addresses limitations, scalability concerns, and future research directions, particularly in the context of emerging automotive zonal controllers and highly integrated system-on-chip platforms. By synthesizing theoretical insights with practical design evidence, this article aims to serve as a comprehensive academic reference on lockstep and redundant multicore fault-tolerant processor architectures.
Copyright (c) 2025 Dr. Michael A. Thornton https://creativecommons.org/licenses/by/4.0
Artificial Intelligence–Driven Organizational Transformation: Integrating Digital Capabilities, Continuous Improvement, and Maturity Models for Sustainable Business Performance
The rapid diffusion of artificial intelligence, advanced analytics, and digital technologies has fundamentally altered how organizations design strategies, structure work, and pursue competitive advantage. While artificial intelligence is often discussed in terms of automation or technical performance, its deeper organizational implications remain insufficiently integrated across management theory, digital transformation research, and continuous improvement traditions. This study develops a comprehensive theoretical analysis of artificial intelligence–driven organizational transformation by synthesizing insights from digital transformation literature, deep learning research, organizational change theory, maturity models, and continuous improvement frameworks such as Lean, Kaizen, and Six Sigma. Drawing strictly on established academic and practitioner references, the article conceptualizes artificial intelligence not as a standalone technological artifact but as an evolving organizational capability embedded within social systems, managerial decision-making, and institutional routines. A qualitative, theory-building methodology is adopted to integrate perspectives from economics, management science, organizational development, and innovation studies. The findings suggest that successful artificial intelligence adoption depends less on algorithmic sophistication and more on organizational readiness, ethical governance, process maturity, and continuous learning mechanisms. The discussion highlights how maturity models provide a structured pathway for aligning artificial intelligence initiatives with organizational growth stages, while continuous improvement philosophies mitigate risks related to bias, misalignment, and resistance to change. The study contributes to the literature by offering an integrative framework that connects artificial intelligence capabilities with organizational evolution and sustainable value creation. Implications for managers, consultants, and policymakers are discussed, alongside limitations and directions for future research.
Copyright (c) 2025 Dr. Michael A. Reynolds https://creativecommons.org/licenses/by/4.0
Lockstep-Based Fault-Tolerant Architectures for Dependable Safety-Critical and Cyber-Physical Computing Systems
The rapid proliferation of safety-critical and cyber-physical systems in domains such as automotive electronics, unmanned aerial vehicles, industrial automation, and intelligent energy infrastructures has intensified the demand for computing platforms that combine high performance with stringent fault tolerance and functional safety guarantees. Contemporary systems increasingly rely on multicore and heterogeneous processor architectures, which, while offering superior computational capabilities, introduce complex reliability challenges stemming from transient faults, permanent hardware failures, electromagnetic interference, and design-induced vulnerabilities. Lockstep-based execution paradigms have emerged as a foundational architectural strategy to address these challenges by enabling timely error detection, fault isolation, and deterministic recovery. This article presents a comprehensive and theoretically grounded investigation of lockstep and lockstep-inspired fault-tolerant architectures, drawing exclusively on the provided body of scholarly and industrial references. The discussion spans classical redundancy principles, including dual-core and triple modular redundancy, as well as modern evolutions such as light lockstep, heterogeneous Arm–RISC-V lockstep systems, dynamically coupled cores, and flexible vector lockstep execution in ultra-low-power clusters. Beyond architectural mechanisms, the article situates lockstep computing within broader system-level considerations, including power supply safety design, compliance with functional safety standards, fault injection methodologies, and emerging application contexts such as autonomous vehicles, UAVs, edge intelligence, and secure energy trading. Through extensive theoretical elaboration, the article identifies critical trade-offs between performance, power efficiency, detection latency, and design complexity, while highlighting persistent gaps in scalability, adaptability, and cross-layer integration. The findings underscore that lockstep architectures, when combined with system-aware design principles and rigorous validation strategies, constitute a central pillar for dependable computing in next-generation safety-critical systems.
Copyright (c) 2025 Dr. Alexander J. Hoffman https://creativecommons.org/licenses/by/4.0
OPTIMIZATION OF THERMAL-ELECTRIC TRANSPORT OF THE INTERFACE IN THE Cdte/Cds – Bi₂Te₃/Sb₂Te₃ MONOLITHIC SYSTEM
This article is aimed at an in-depth analysis of the thermal-electrical transport processes that occur when CdTe/CdS-based photovoltaic (PV) layers and Bi₂Te₃/Sb₂Te₃ thermoelectric (TE) modules are combined into a single monolithic structure. Excess heat generated in PV elements can be converted into electrical energy directly through the TE module, however, factors such as diffusion between materials, thermomechanical stress, and high thermal resistance of the interface reduce the efficiency of such integration. Therefore, it is proposed to use nano-thick, thermally conductive and electrically insulating layers such as h-BN or AlN as interfaces. During the study, the thickness, thermal resistance, diffusion-limiting properties, and energy bandgap compatibility of these nano-interfaces were evaluated through computer modeling. The results obtained showed that the correct choice of interface can reduce heat losses in PV–TE systems, increase the power generation capacity of the TE module, and improve the overall efficiency of the hybrid system. Thus, the study scientifically confirms that interface engineering is one of the key factors for high efficiency and stable operation in monolithic PV–TE energy systems.
Copyright (c) 2025 Kasimakhunova Anarkhan Mamasadikovna https://creativecommons.org/licenses/by/4.0
Optimizing Supply Chain Performance Through Vendor Development, Strategic Sourcing, and Digital Integration
The contemporary supply chain landscape is characterized by increasing complexity, rapid technological advancements, and heightened pressure for operational efficiency and sustainability. This research investigates the synergistic effects of vendor development, strategic sourcing, and digital integration on optimizing supply chain performance. Drawing upon the extensive literature on purchasing strategies, supply chain resilience, and Industry 4.0 adoption, this study articulates a comprehensive framework that integrates theoretical and practical dimensions of supply chain management. Key themes include the strategic role of vendor development in mitigating supply chain risks, the application of Kraljic’s purchasing matrix to categorize supplier relationships, and the transformative impact of digital technologies such as predictive analytics, artificial intelligence, and Industry 4.0 principles. The study identifies critical challenges in achieving cost optimization, quality improvement, and supply chain agility, and explores the interplay between sustainable supply chain practices and operational efficiency. Findings indicate that organizations implementing structured vendor development programs, leveraging strategic sourcing decisions, and integrating digital technologies can achieve significant improvements in cost reduction, delivery performance, risk management, and overall competitiveness. Furthermore, the research highlights gaps in the literature regarding the measurement of long-term benefits from vendor development initiatives and the operationalization of Industry 4.0 technologies in diverse supply chain contexts. The study concludes by proposing a conceptual model linking strategic sourcing, vendor development, and digital integration to enhanced supply chain performance, providing a roadmap for managers and policymakers seeking to strengthen supply chain resilience and efficiency in dynamic business environments.
Copyright (c) 2025 Dr. Alexander Hartmann https://creativecommons.org/licenses/by/4.0
Enhancing Software Supply Chain Security: Comprehensive Analysis of Vulnerabilities, Dependency Management, and SBOM Strategies
The rapid proliferation of open-source software and the extensive integration of third-party dependencies in contemporary software ecosystems have transformed the software supply chain into a critical area of cybersecurity concern. The increasing complexity of these ecosystems exposes software products to diverse vulnerabilities, including dependency-based attacks, misconfigurations, and targeted supply chain compromises. This research presents a comprehensive analysis of software supply chain security, focusing on the theoretical and practical challenges of vulnerability assessment, dependency management, and the implementation of Software Bill of Materials (SBOM) solutions. By synthesizing findings from recent empirical studies and historical analyses, the paper elucidates the structural and operational weaknesses in open-source ecosystems, such as npm and Apache, and examines the efficacy of automated vulnerability detection, SBOM generation, and LLM-based vulnerability sourcing. This study adopts a descriptive methodological framework, leveraging theoretical constructs like attack trees, dependency graphs, and supply chain threat modeling to systematically evaluate the current security landscape. Findings indicate that while tools for automated vulnerability scanning and SBOM generation provide measurable improvements in visibility and traceability, they are constrained by limitations in detection accuracy, dependency coverage, and the dynamic nature of software evolution. Additionally, the research highlights the sociotechnical dimensions of software supply chain security, emphasizing the roles of developer practices, community governance, and stakeholder perceptions in mitigating risk. The discussion integrates empirical insights with conceptual analysis to propose strategic and operational recommendations, including standardized SBOM practices, continuous dependency monitoring, and the integration of AI-assisted vulnerability identification within development workflows. This paper contributes to the literature by offering a holistic, theoretically grounded, and practically relevant perspective on securing software supply chains, underscoring the need for multi-layered, proactive, and ecosystem-aware approaches.
Copyright (c) 2025 https://creativecommons.org/licenses/by/4.0
Emerging Paradigms in Dark Web Cyber Threat Intelligence: Methodologies, Mechanisms, and Proactive Defense Strategies
The increasing sophistication of cyber threats necessitates advanced intelligence gathering strategies to protect digital assets and organizational infrastructures. This study explores the emerging paradigms in dark web cyber threat intelligence (CTI), emphasizing the integration of open, deep, and dark web data sources for proactive threat mitigation. By synthesizing contemporary methodologies, including crawler architectures, artificial intelligence-based entity recognition, and automated threat intelligence frameworks, the research delineates a comprehensive approach to real-time threat monitoring. The work further examines the operationalization of threat intelligence across industrial, governmental, and organizational domains, highlighting challenges associated with data quality, timeliness, and adversarial countermeasures. Key findings indicate that multi-source intelligence aggregation significantly enhances predictive capabilities, reduces response times to emerging threats, and supports dynamic defense strategies. Limitations include the scalability of automated systems, ethical considerations in dark web monitoring, and the evolving sophistication of cyber adversaries. The study concludes by proposing a hybrid framework combining human analytic expertise with automated, AI-powered threat intelligence systems, aimed at strengthening the resilience of critical information infrastructures.
Copyright (c) 2025 Johnathan Meyer https://creativecommons.org/licenses/by/4.0
Advancing Transparent and Human-Centered Artificial Intelligence: A Comprehensive Review of Explainable AI Theories, Methods, and Applications
Explainable Artificial Intelligence (XAI) has emerged as a central paradigm in contemporary artificial intelligence research, driven by the growing deployment of machine learning systems in high-stakes domains such as healthcare, finance, governance, and autonomous systems. While predictive accuracy has traditionally dominated the evaluation of machine learning models, increasing concerns regarding opacity, accountability, fairness, trust, and ethical compliance have exposed fundamental limitations of black-box approaches. This article presents a comprehensive, publication-ready research study that synthesizes and critically elaborates on the theoretical foundations, taxonomies, methodologies, and application-driven implications of XAI, based strictly on established scholarly literature. Drawing from foundational surveys, conceptual frameworks, and domain-specific studies, the article examines explainability from multiple perspectives, including technical model interpretability, human-centered explanation effectiveness, causality and counterfactual reasoning, knowledge-based representations, and stakeholder-oriented requirements. Particular attention is given to the tension between model complexity and interpretability, the distinction between intrinsic and post-hoc explanations, and the evolving role of XAI in regulated and safety-critical environments. Methodologically, the study adopts a structured qualitative synthesis approach, integrating comparative analysis and conceptual reasoning to uncover patterns, gaps, and unresolved challenges within the existing body of work. The results highlight that explainability is not a singular technical property but a socio-technical construct shaped by context, audience, and purpose. The discussion extends these findings by addressing limitations of current XAI methods, including evaluation ambiguity, potential for misleading explanations, and insufficient alignment with human reasoning. The article concludes by proposing future research directions toward responsible, human-aligned, and causally grounded XAI systems. Overall, this work contributes an in-depth, theoretically rich, and integrative perspective intended to guide researchers, practitioners, and policymakers toward more transparent and trustworthy artificial intelligence.
Copyright (c) 2025 Dr. Eleanor Whitfield https://creativecommons.org/licenses/by/4.0
Integrating Large Language Model–Driven Code Generation and Business Process Automation: Impacts on Enterprise Systems Performance and Maintainability
The increasing maturity of large language model (LLM)–based code generation tools offers unprecedented opportunities to accelerate software development, particularly in domains requiring rapid prototyping, business process automation, and enterprise application deployment. This paper examines the theoretical and practical implications of integrating LLM-driven coding assistants with enterprise systems architecture, focusing on performance impacts, maintainability, and alignment with business process management (BPM) objectives. Drawing on publicly documented features of tools such as GitHub Copilot and Cursor, as well as established best practices in software engineering and enterprise resource planning (ERP), we perform a conceptual comparative analysis of traditional development workflows versus LLM-augmented workflows. We further analyze empirical findings from recent performance studies of CRUD (Create, Read, Update, Delete) operations in Java-based persistence frameworks. The study explores how LLM-generated boilerplate accelerates initial development yet may introduce inefficiencies or readability challenges if not followed by disciplined refactoring (based on principles from refactoring literature). We then connect these technical outcomes to enterprise-level business process objectives — such as supply chain modeling, automation through robotic process automation (RPA), and ERP integration — to assess the broader organizational impacts. We find that while LLM-driven development significantly reduces development time and lowers the barrier to entry for non-expert developers, there exist trade-offs in runtime performance, maintainability, and scalability — especially when database interactions are abstracted without consideration of optimized data access patterns. To manage these trade-offs, we propose a hybrid workflow combining LLM-assisted code generation, systematic refactoring, and performance validation as part of the release pipeline. We conclude with a roadmap for future empirical evaluation and integration strategies to maximize business value while minimizing technical debt.
Copyright (c) 2025 Dr. Arjun Patel https://creativecommons.org/licenses/by/4.0
Integrated Security- and Quality‑Aware Automation Framework for Mobile Applications: Bridging Traditional Test Automation and LLM‑Enhanced App Security
The rapid proliferation of mobile applications combined with the emerging integration of Large Language Models (LLMs) into mobile clients has given rise to new security, privacy, and quality‑assurance challenges. Traditional mobile testing frameworks—centered around GUI automation, functional regression, and crash detection—are insufficient to address vulnerabilities introduced by LLM‑enabled features, such as prompt injection, data leakage, and adversarial manipulations. In this article, we propose an integrated, hybrid framework that unites conventional mobile automation testing techniques with security‑ and privacy‑oriented analyses tailored for LLM‑enhanced applications. Our methodology synthesizes static and dynamic analysis, GUI and workflow testing, along with machine-learning based anomaly detection for runtime behaviors, forming a unified pipeline adaptable across diverse mobile platforms. We detail the design considerations, describe how classical techniques like keyword‑driven testing can be extended to meet security demands, and discuss how machine-learning models (including lightweight on-device inference) can scale across heterogeneous hardware. We also analyze the limitations of our approach and outline a roadmap for future enhancements, including explainability, federated learning, and prevention‑oriented strategies. The proposed framework seeks to promote both software quality and user privacy/security as first-class citizens in the age of AI‑augmented mobile applications.
Copyright (c) 2025 Dr. Anjali Rao https://creativecommons.org/licenses/by/4.0
Advanced Computational Intelligence and Predictive Modeling in Real-Time Systems: Integrating Heuristic Optimization, Deep Learning, and Large Language Models
The rapid evolution of computational intelligence has profoundly transformed the landscape of real-time systems, predictive modeling, and human-computer interaction. Modern applications demand the integration of heuristic optimization algorithms, deep learning architectures, and large language models (LLMs) to address increasingly complex scheduling, predictive, and control challenges. This study presents a comprehensive examination of mixed-heuristic quantum-inspired algorithms for multiprocessor task scheduling, multi-scale convolutional recurrent models for environmental prediction, and advanced LLM-based interventions in social and educational contexts. A focus is placed on methodologies such as CNN-LSTM-Attention frameworks for time-series prediction, BERT-XGBoost models for psychological state analysis, and normal vector-assisted mapping for robotic navigation. Further, the study explores real-time video tracking algorithms leveraging convolutional neural networks, ant colony-based path planning in logistics robotics, and multi-modal data analysis for structural assessment in tunnel engineering. The integration of LLMs for social network crisis intervention and knowledge-enhanced decision-making is evaluated in the context of operational efficacy and ethical considerations. Through extensive theoretical elaboration, this research articulates the synergistic effects of algorithmic optimization, deep learning, and generative intelligence in enhancing real-time system performance, predictive accuracy, and human-computer collaboration. The implications of these techniques are critically analyzed, highlighting both the potential benefits and the inherent limitations, particularly concerning data privacy, computational overhead, and real-world deployment constraints. This work provides a foundational framework for the future development of hybrid computational intelligence systems capable of robust, scalable, and ethically responsible operation in complex, dynamic environments.
Copyright (c) 2025 Dr. A. Vance https://creativecommons.org/licenses/by/4.0
Resilient Semiconductor Supply Chains: Strategic Planning, Risk Mitigation, and Adaptive Production Networks in an Era of Reshoring and Disruption
This article presents an integrative and original research treatment of semiconductor supply chain resilience, master planning, production planning, and demand fulfilment in the context of contemporary forces of regionalisation and reshoring. Drawing on a curated set of empirical studies, modelling surveys, and applied analyses from the fields of operations research, supply chain management, and regional economic policy (Mönch et al., 2018a; Mönch et al., 2018b; Ivanov et al., 2017), the paper synthesises theoretical constructs with practical managerial interventions and proposes a comprehensive, descriptive framework for strategic decision making under uncertainty. The research articulates how capacity reservation contracts, forecast updating mechanisms, emergency ordering policies, and cooperative supplier development interact to shape production planning outcomes (Cheaitou & Cheaytou, 2019; Johansen, 2019; Talluri et al., 2010). The role of network structure, simulation-based analysis, and resilience-sustainability interfaces in guiding contingency planning is examined (Ivanov, 2018; Ivanov et al., 2017). The paper further explores the drivers and implications of geographic reorientation of value chains—regionalisation and reshoring—through the case of Japan’s semiconductor industry and contemporary policy pressures (Kamakura, 2022; Lulla, 2025). Special emphasis is placed on demand fulfilment strategies in periodic-review inventory systems with stochastic lead times and emergency order options, on manufacturer-supplier cooperation under risk, and on tool allocation strategies to manage work-in-process and cycle time (Johansen & Thorstenson, 2014; Chen-Fu Chien et al., 2020). The study concludes with an extended discussion of managerial implications, trade-offs among resilience, cost, and sustainability objectives, and an agenda for future research that foregrounds simulation-based experimentation, multi-agent coordination, and policy-relevant modelling. Keywords: semiconductor supply chain, resilience, reshoring, capacity reservation, emergency orders, production planning, simulation.
Copyright (c) 2025 Muradov Akramjon Abdusattarovich https://creativecommons.org/licenses/by/4.0
Resilient and Adaptive Semiconductor Supply Chains: Integrating Risk Management, Digital Twins, and Strategic Reconfiguration
The semiconductor industry has emerged as a critical backbone of modern technology, underpinning applications from consumer electronics to defense systems. Recent disruptions, ranging from global financial crises to pandemic-induced shortages, have exposed vulnerabilities in global semiconductor supply chains, emphasizing the need for resilience, adaptability, and strategic reconfiguration. This study synthesizes existing research on supply chain risk management, digital twin integration, and post-pandemic strategic shifts to provide a comprehensive framework for enhancing semiconductor supply chain resilience. Through an extensive literature-based approach, the paper examines quantitative and qualitative risk management models, supply chain visibility mechanisms, and the role of emerging regulations, such as the European Union Chips Act, in reshaping value chains. Additionally, it evaluates the feasibility of reshoring strategies for high-tech production, including graphics processing units (GPUs), to strengthen domestic and regional manufacturing capabilities. The study highlights the importance of integrating multiple theoretical perspectives, such as operations management, production planning, and strategic policy analysis, to mitigate risks and optimize performance. The findings suggest that adopting digital twin technologies, enhancing transparency, and employing robust risk assessment methodologies are critical for maintaining operational continuity and competitive advantage. Furthermore, the analysis underscores the interplay between industrial policy, strategic sourcing, and global-local supply chain trade-offs, offering a multidimensional understanding of resilience in turbulent and uncertain environments. This work contributes to both theoretical advancement and practical policy formulation, providing actionable insights for managers, policymakers, and researchers seeking to design robust, adaptive, and sustainable semiconductor supply chains in an era of unprecedented disruption.
Copyright (c) 2025 Umarov Adkham Akparalievich https://creativecommons.org/licenses/by/4.0
Resilient Fog-Cloud Architectures and Fault-Tolerant Test Infrastructure for Large-Scale GPU Manufacturing: Integrating Edge Intelligence, Energy Prediction, and Verification Paradigms
This article synthesizes contemporary conceptual foundations and applied methodologies for designing resilient fog–cloud computing architectures and fault-tolerant test infrastructure targeted at large-scale GPU manufacturing. The abstract summarizes the research problem, methodological approach, principal findings, and implications. Background: modern GPU manufacturing and deployment increasingly rely on distributed computation across cloud, fog, and edge tiers for tasks including in-line inspection, predictive power management, real-time quality control, and cryptographically secure telemetry aggregation (Prakash, Suresh, & Dhinesh Kumar, 2019; Deepika & Prakash, 2020). Problem statement: existing test infrastructures for GPUs often fail to simultaneously satisfy the competing demands of scalability, low latency, energy efficiency, and rigorous formal verification (Designing Fault-Tolerant Test Infrastructure for Large-Scale GPU Manufacturing, 2025; Huang, Tsai, Paul, & Chen, 2005). Methods: this work develops an integrative framework combining fog computing paradigms, machine-learning based power prediction, hybrid cloud storage and serverless HPC patterns, and formal verification tools (Labelled Transition System Analyser; Lambers, Ehrig & Orejas, 2006; Lohmann, Sauer & Engels, 2005). Results: descriptive analysis demonstrates how layered redundancy, consensus-inspired decision rules, and blockchain-augmented telemetry can jointly improve fault tolerance, reduce energy volatility, and maintain throughput under realistic manufacturing perturbations (Iyer, 2020; Yehia & Aljaafreh, 2023). Discussion: the paper interprets findings in light of trade-offs between latency, trust, and computational cost, examines limitations of current verification approaches, and maps a research agenda for automated model checking and fog-native testing. Conclusion: by synthesizing theoretical and applied work across distributed computing and verification communities, the proposed architecture offers a practical route to robust, cost-effective, and verifiable GPU testbeds capable of meeting future manufacturing scale-up.
Copyright (c) 2025 Dr. Marcus R. Ellison https://creativecommons.org/licenses/by/4.0
Multiscale Numerical Control and High-Performance Simulation for Continuous Casting: Integrating Model Predictive Control, Lattice Boltzmann Methods, and GPU Acceleration
This article presents an integrative, theoretically rigorous, and methodologically detailed exposition on the design, analysis, and numerical implementation of advanced control strategies for continuous casting processes, emphasizing model predictive control for nonlinear parabolic partial differential equation (PDE) systems, lattice Boltzmann method (LBM) based fluid–thermal simulation, and high-performance computing (HPC) implementations on graphics processing units (GPUs). By synthesizing methodological advances from state-of-the-art control theory applied to unsteady PDEs (Yu et al., 2023; Wang et al., 2016; Yu et al., 2018) with kinetic-based fluid modelling and thermodynamics captured by lattice Boltzmann frameworks (d’Humières et al., 2002; He et al., 1998; Lallemand & Luo, 2003), and the practical acceleration strategies using CUDA-enabled GPU computing (Micikevicius, 2009; NVIDIA, 2010; Mudigere, 2009), the paper articulates a comprehensive pipeline: from mathematical problem formulation and discretization strategy, through controller design and stability considerations, to efficient implementation patterns that respect memory access, parallelism, and numerical accuracy on modern heterogeneous architectures. The article places particular emphasis on handling convective terms in unsteady parabolic PDEs, the computational advantages and limitations of multiple-relaxation-time LBM for thermal-acoustic fidelity, and practical considerations when migrating model predictive control algorithms to GPUs for real-time or near-real-time industrial use (Wang et al., 2019). The narrative critically examines the interplay between model fidelity, controller robustness, numerical stability, and computational throughput—highlighting trade-offs, potential failure modes such as longitudinal crack formation in hypoperitectic steels during solidification (Konishi et al., 2002), and pathways for mitigating these through control-informed cooling strategies (Wang et al., 2016). The synthesis culminates in an extended methodological blueprint suitable for researchers and practitioners seeking to develop publication-ready, production-grade simulation-control systems for continuous casting and analogous thermofluid processes.
Copyright (c) 2025 Anil R. Menon https://creativecommons.org/licenses/by/4.0
GPU-Accelerated Monte Carlo Methods for Proton Therapy: Integrating Heterogeneous Runtime Systems and Unified Memory Strategies for Clinical-Grade Dose Calculation
This article presents a comprehensive, publication-ready investigation into the integration of GPU-accelerated Monte Carlo (MC) dose calculation methods for proton therapy with modern heterogeneous runtime systems and unified memory strategies. The work synthesizes principles from GPU programming, runtime compilation, managed runtime systems, and medical physics Monte Carlo simulations to propose a cohesive framework for clinical-grade, high-throughput proton dose calculation. The abstract outlines the motivation: proton therapy demands highly accurate dose calculations that account for complex particle transport physics while delivering results within clinical time constraints. Traditional CPU-based MC codes offer high fidelity but are limited by throughput; GPU implementations have demonstrated orders-of-magnitude speedups, yet bring challenges in memory management, precision, platform heterogeneity, and security. This paper reviews the computational and physical foundations of MC simulation in proton therapy, examines GPU programming models and thread hierarchies, surveys existing GPU-based MC systems and verification/validation efforts, analyzes unified memory and its performance implications, and proposes a methodology that couples just-in-time (JIT) GPU compilation, managed runtimes, and careful algorithmic restructuring to reconcile precision, performance, and maintainability. The results section provides a descriptive analysis of expected performance gains, trade-offs in memory strategies, and implications for clinical deployment. In the discussion, limitations, potential failure modes, and avenues for future work—including validation workflows, regulatory considerations, and hybrid CPU–GPU orchestration—are explored in depth. The conclusion synthesizes these threads into actionable recommendations for researchers and system designers seeking to produce clinically viable, reproducible, and secure GPU-accelerated Monte Carlo proton therapy dose engines. Keywords: proton therapy, Monte Carlo simulation, GPU acceleration, unified memory, just-in-time compilation, heterogeneous runtime, clinical dose calculation.
Copyright (c) 2025 Dr. Marcellus V. Carter https://creativecommons.org/licenses/by/4.0
Sustainable Textile-Based Nonwoven Composites for Thermal and Acoustic Insulation: Materials, Methods, Performance, and Circular Pathways
Background: Growing concerns about energy efficiency, resource depletion, and the environmental footprint of building and transport sectors have directed attention toward sustainable insulation solutions derived from textile-based materials and natural fibres. Textile-derived nonwovens, recycled fibres, and agricultural waste streams present promising raw-material sources that can simultaneously address acoustic and thermal performance requirements while aligning with circular-economy strategies. Objective: This article synthesizes contemporary literature on textile-based composites and nonwoven materials for thermal and acoustic insulation, critically examines material selection and processing pathways, proposes detailed experimental and characterization methodologies that are consistent with current best practice, and integrates insights on circularity, energy sustainability, and techno-economic considerations to provide a comprehensive roadmap for academic and industrial stakeholders. Methods: We present an integrative methodological framework describing material sourcing (recycled nonwovens, agricultural residues such as luffa, and natural fibre blends), nonwoven manufacturing and consolidation techniques (carding, needlepunching, hydroentangling, thermal bonding, and environmentally compatible binders), and performance characterization (thermal conductivity, heat capacity proxies, sound absorption coefficients across broad frequency bands, moisture sorption, durability, and fire performance proxies). We emphasize descriptive, reproducible protocols and quality-control concepts. Results: Collating evidence from experimental reports and material characterization studies, textile-based nonwovens and composites constructed from recycled cotton/polyester blends and natural fibres demonstrate competitive sound absorption across mid-to-high frequencies and tunable thermal properties through density and porosity control. Agricultural fibres such as luffa show notable porosity and tortuosity characteristics that improve broadband acoustic absorption when structured as fibrous mats. Thermal performance benefits from low thermal conductivity inherent to porous fibrous structures, but moisture sensitivity and flammability remain obstacles that need mitigation. Conclusions: Textile-based nonwoven composites are well-positioned to contribute to energy-efficient building envelopes and lightweight transport insulation systems when design practices emphasize porosity control, hybridization with other natural or mineral fillers, moisture management, and circular end-of-life planning. Policy-aligned industrial practices, integration with recycling infrastructures, and standards development will be central to scaling these solutions. Implications: This paper offers researchers and practitioners a detailed descriptive methodological baseline and critical synthesis to design, test, and upscale sustainable textile-based insulation solutions that balance performance, life-cycle impacts, and circularity goals.
Copyright (c) 2025 Dr. Emilio Santos https://creativecommons.org/licenses/by/4.0
Sustainable Acoustic and Thermal Insulation from Natural and Recycled Fibers: Integrating Circular-Economy Principles into Building Material Science
This article presents a comprehensive, theory-driven examination of sustainable acoustic and thermal insulation materials derived from natural fibers, agricultural by-products, recycled textiles, and end-of-life household materials. The work synthesizes empirical results and conceptual frameworks from recent investigations into thermal degradation, moisture absorption, antibacterial behavior, acoustic absorption, and composite-reinforcement performance to produce an integrated perspective on how circular-economy principles can be operationalized in building envelopes and interior acoustic treatments. The abstracted narrative spans material selection rationale, physicochemical and microbiological durability, hygrothermal interactions, and acoustic performance mechanisms. It further situates passive insulation strategies within waste-management frameworks and regulatory contexts to propose practical pathways for large-scale adoption. Methodological discussion emphasizes reproducible, text-based experimental protocols and performance evaluation metrics widely reported in the literature. The results section offers descriptive analyses linking fiber morphology, surface chemistry modifications, and panel manufacturing parameters to measurable thermal and acoustic outcomes. The discussion critically examines trade-offs between thermal conductivity and sound absorption, the role of moisture and biodegradation in long-term performance, and the socio-technical barriers to mainstreaming low-cost bio-based insulators in vulnerable contexts. Limitations and future directions are articulated with explicit attention to lifecycle analysis, standardization needs, and scale-up considerations. This integrative article intends to inform material scientists, building engineers, policymakers, and circular-economy stakeholders seeking to advance sustainable insulation technologies.
Copyright (c) 2025 John E. Harrington https://creativecommons.org/licenses/by/4.0
Digitalising Trust: Regulatory Design, Risk, and Inclusion at the Intersection of AI, Big Tech, and Banking
This article examines the multifaceted transformation of the finance sector driven by digitalisation, artificial intelligence (AI), and the increasing participation of big tech firms in banking services. It synthesises theoretical frameworks of financial regulation with empirical and policy-oriented literature to propose an integrative understanding of regulatory challenges and policy design choices. Drawing on interdisciplinary sources, the paper advances three core arguments. First, the digitalisation of finance fundamentally alters information asymmetries, market structure, and the set of systemic risks that regulators must confront, necessitating an adaptive regulatory stance that blends traditional prudential tools with more dynamic, data- and algorithm-aware supervision (Arner, Barberis & Buckley, 2015; BCBS, 2024). Second, the entry and expansion of big tech and fintech into credit markets reconfigures competitive dynamics and consumer relationships, offering both inclusionary opportunities and concentrated-risk pathways that demand tailored oversight—particularly regarding data governance, model transparency, and adverse action in automated credit decisioning (Cornelli et al., 2023; Barakova, Ehrentraud & Leposke, 2024; CFPB, 2022). Third, governance of AI in finance sits at the confluence of cross-border regulatory fragmentation and rapidly evolving technological capacities; effective policy requires harmonised principles, robust supervisory capacity building, and mechanisms for algorithmic accountability that preserve innovation while protecting financial stability and consumer rights (BCBS, 2022; Calabia, 2024; CSET, 2024). The paper offers a detailed discussion of regulatory instruments—ranging from transparency mandates and model validation standards to supervisory sandboxes and macroprudential augmentation—and elaborates on trade-offs, implementation constraints, and research priorities. It concludes with policy recommendations emphasising multi-level regulatory coordination, investment in regulatory technology (RegTech), and a principled yet flexible framework for AI governance that aligns prudential aims with inclusion and market contestability.Digitalisation; Artificial Intelligence; Big Tech; Financial Regulation; Credit; Consumer Protection; Supervisory Technology
Copyright (c) 2025 Dr. Alejandro García https://creativecommons.org/licenses/by/4.0
Adaptive Regulatory Architectures for FinTech Resilience: Integrating Dynamic Environmentalism, Responsive Regulation, and Machine Learning–Enhanced Policy Review
This article advances a comprehensive theoretical and applied framework for adaptive regulatory architectures tailored to the FinTech sector. It synthesizes interdisciplinary literatures on adaptive management and dynamic environmentalism, responsive regulation, institutional inquiry following crises, and contemporary FinTech regulatory debates to propose a multi-layered model for regulatory resilience. Drawing upon foundational legal and policy texts (Adler, 2016; Ayres & Braithwaite, 1994), institutional analyses of post-crisis inquiry mechanisms (Balleisen et al., 2017a; Balleisen et al., 2017b), and recent FinTech-focused empirical and conceptual studies (Aarnalaw, 2024; Alam, 2022; Alalwan et al., 2024; Anyfantaki, 2016), the study articulates a methodologically text-based approach that integrates retrospective review, machine learning–assisted multi-policy analysis (Singh, 2025), and responsive, graduated enforcement. The paper explicates the theoretical underpinnings of regulatory adaptability, details an operational methodology for policy design and retrospective evaluation, and presents descriptive results from synthesized insights across the cited works. It discusses practical implications for regulators, highlights limitations of purely algorithmic governance, and delineates a future research agenda focusing on empirical validation, cross-jurisdictional comparison, and ethical safeguards. The overarching claim is that an adaptive regulatory architecture—anchored in dynamic environmentalism, supported by responsive governance mechanisms, and augmented with careful applications of machine learning for policy analysis—can better manage systemic risks, promote innovation, and enhance public trust in FinTech ecosystems (Adler, 2016; Ayres & Braithwaite, 1994; Singh, 2025; Alam, 2022).
Copyright (c) 2025 Dr. Elena Márquez https://creativecommons.org/licenses/by/4.0
Adaptive Governance of Intelligent Financial Systems: Integrating Historical Regulatory Lessons with Machine Intelligence for Robust Financial Stability
The rapid integration of artificial intelligence (AI) and machine learning (ML) into financial systems has produced opportunities for efficiency, risk identification, and dynamic decision-making, while simultaneously introducing systemic, operational, and ethical challenges for regulators and institutions. This paper synthesizes lessons from historical banking failures and regulatory reforms with contemporary analyses of AI's role in finance to propose an adaptive governance framework for "intelligent financial systems." Drawing on the legacies of crisis-driven regulatory change, classical inquiries into bank failure, and emerging international guidance on model risk and AI deployment, the study maps the parallels and tensions between past regulatory interventions and present AI-driven disruptions (Acharya et al., 2011; Alston et al., 1994; Benston & Kaufman, 1997). It further collates contemporary institutional and scholarly perspectives on AI in financial services to inform a methodology for multi-policy analysis and model risk management (Bank of England, 2022; BoE-PRA, 2023; Aldasoro et al., 2024; Singh, 2025). The paper's methodology uses a theory-driven synthesis and comparative regulatory-historical analysis combined with conceptual policy simulation to derive governance principles and operational controls appropriate for banks, central banks, and supervisory authorities. Results emphasize (a) the necessity of layered controls that combine traditional microprudential tools with machine-aware supervisory approaches, (b) the critical role of transparency and explainability in model governance, and (c) the importance of systemic scenario analysis that explicitly models AI-induced feedback loops and concentration risks (Bradford, 1935; Bratton, 2003; Bank of England & FCA, 2024). The discussion unpacks trade-offs between innovation and safety, the limits of rule-based versus principle-based regulation in AI contexts, and the institutional capacity-building required to implement adaptive regulation. Limitations are acknowledged regarding empirical calibration and evolving technological capacity; suggested future work includes empirical validation of the proposed governance taxonomy, development of sector-specific AI stress-testing protocols, and international cooperation mechanisms. The conclusion outlines actionable recommendations for policymakers and institutions to foster resilient, accountable, and innovation-friendly intelligent financial systems anchored in historical insight and modern model-risk practices.
Copyright (c) 2025 Rohit K. Menon https://creativecommons.org/licenses/by/4.0
Intelligent Supply Chain Management: Leveraging AI for Visibility, Resilience, and Sustainable Industry 5.0 Operations
The rapid evolution of supply chain systems under Industry 4.0 and the emerging paradigms of Industry 5.0 has emphasized the critical role of digitalization, predictive intelligence, and sustainable practices in logistics and operations management. Artificial intelligence (AI) has emerged as a transformative force, enabling advanced predictive analytics, real-time decision-making, supply chain visibility, and risk mitigation across diverse sectors. This study presents a comprehensive exploration of AI applications in modern supply chain management (SCM), integrating insights from logistics, finance, sustainability, and resilience frameworks. The research synthesizes findings from empirical studies, systematic reviews, and applied industry cases, highlighting AI-driven optimization in agricultural, manufacturing, and retail supply networks. Key methodologies discussed include neural networks, reinforcement learning algorithms, predictive analytics, and blockchain integration, with a focus on their contribution to supply chain visibility, performance, and resilience. Challenges such as data heterogeneity, implementation barriers, and ethical considerations are critically analyzed. Furthermore, this research emphasizes the interplay between AI networks and sustainable supply chain finance, illustrating the dual objectives of efficiency enhancement and environmental responsibility. The findings demonstrate that AI not only streamlines operational workflows but also fosters strategic alignment, inter-organizational collaboration, and adaptive risk management, creating a robust foundation for resilient, sustainable, and intelligent supply chains. Implications for policymakers, practitioners, and researchers are discussed, with recommendations for future research directions targeting Industry 5.0 ecosystems. This work advances the theoretical and practical understanding of AI’s integrative role in SCM, establishing a roadmap for leveraging technological innovations to achieve comprehensive supply chain optimization.
Copyright (c) 2025 Dr. Matteo Ricci https://creativecommons.org/licenses/by/4.0