Journal of Information Systems Engineering and Management
Journal of Information Systems Engineering and Management Journal of Information Systems Engineering & Management is published quarterly ISSN (Online): 2468-4376 DOI: https://doi.org/10.55267/iadt
Impact of Institutional Financing on Small Enterprise Growth: Evidence from India and Meghalaya
The competitive strength of small business depends largely on the availability of funds for implementation of appropriate strategy. The paper explores the critical relationship between financial institutions and the growth of small enterprises, with a specific focus on India and the state of Meghalaya. It examines how institutional financing impacts small-scale industrialization, addressing key research questions such as the correlation between small enterprise growth and institutional financing, the influence of financial factors, and the role of bank branches and credit-deposit (C/D) ratios. The study employs correlation, and regression models, over a 37-year period (1976–2013). The findings reveal a significant positive correlation between institutional financing and small enterprise growth in terms of units, capital investment, and employment, both at the national level and in Meghalaya. However, challenges such as credit constraints, lack of proper documentation, and limited entrepreneurial skills hinder the sector's development, particularly in Meghalaya. The study also highlights the need for improved financial accessibility, capacity building, and policy measures to address these barriers. It concludes that while institutional financing plays a pivotal role in fostering small enterprise growth, a more inclusive and supportive financial ecosystem is essential for sustained development.
Bridging Mathematics Achievement Gaps: An Intersectional Analysis of Vulnerable Student Populations in U.S. Schools
Persistent inequalities in mathematics achievement continue to represent one of the most persistent forms of educational inequality worldwide. Despite decades of policy reforms aimed at improving inclusive education, students with disabilities, economically disadvantaged students and English learners continue to perform below the general student population in mathematics assessments. These disparities limit not only academic success but also long-term access to higher education, STEM careers and socioeconomic mobility. This study examines variations in mathematics achievement gaps among students with disabilities, economically disadvantaged students and English learners during the 2021-2022 academic year. Using large-scale state-level assessment data, the study applies comparative statistical analysis, including analysis of variance (ANOVA), grade-level trend evaluation and regression modeling, to examine demographic disparities and intersectional effects across educational stages. The analysis captures both between-state differences and within-grade performance patterns to provide a system-level understanding of achievement inequality. The findings reveal substantial and statistically significant disparities across demographic groups and states. Achievement gaps widen progressively as students advance through grade levels, with the most pronounced declines occurring during middle and higher grade levels. Students experiencing intersecting disadvantages, particularly disability combined with economic hardship or language barriers, demonstrate consistently lower mathematics proficiency outcomes when compared with students facing a single challenge. Results further indicate that variations in educational resources, instructional support systems and inclusive teaching practices contribute significantly to observed performance differences. This study concludes that closing mathematics achievement gaps requires investments in inclusive education, targeted instructional support and policies designed to ensure that every student has a fair opportunity to succeed. Therefore, the study recommends that Governments at both federal and state levels should therefore prioritize funding models that direct additional resources to schools and regions where achievement gaps are most pronounced.
IoT-Based Energy Efficiency and Management
The increasing complexity of energy requirements in the residential and industrial sphere has added stress to the need of scalable and autonomous solutions to ensure efficient handling of energy. Scalability is also difficult in scenarios where energy consumption is diffuse and changing, especially those sectors of industry where power usage at distant locations must be observed on an ongoing basis. The unending automation regulations created based on the consumption pattern would require immediate usages in real condition without reliance on maintenance by human beings. The system should be updated automatically in order to have optimal energy configurations that should contain operational best practices in coping with network topology change as well. The proposed solution takes up these issues through extensive study using Internet of Things (IoT) frameworks and machine learning-based approaches. The system has dynamically network energy supervisory capabilities with distributed system energy management capabilities through its flexible system architecture. Industry standards are shaped around research-based behavioural patterns to act as a device classification mechanism and to maximise on resource distribution. Both smart industry energy managers and autonomous smart home systems will be able to track and manage various equipment of operation using the centralized system but unlike that of the autonomous smart home systems which without any human assistance reduces wastes. The combination of IoT and data analytics as a working process serves as a tool to monitor the industrial energy consumption and reduce the resources overpower and contribute to the ecological processes in all the environments.
A Gradient Boosting–Based Framework for Fraud Detection in Real-Time Digital Payment Systems
Digital payment systems have grown rapidly with the expansion of mobile banking and online financial services. This growth has also increased the risk of fraudulent transactions in real-time payment environments. Detecting such activities quickly and accurately has become an important challenge for financial institutions. This study proposes PerfEval-FraudML, a machine learning framework designed to identify fraudulent transactions in digital payment systems. The framework analyzes transaction features such as transaction amount, velocity score, device type, transaction category, and previous fraud indicators to classify transactions as legitimate or fraudulent. The proposed approach applies a Gradient Boosting–based classification model to learn patterns associated with fraudulent behavior. The model is trained and evaluated using a synthetic transaction dataset that reflects common financial transaction characteristics. During the experimental evaluation, the model achieved an accuracy of 92%, an F1-score of 0.89, and an AUC-ROC value of 0.94, indicating strong capability in distinguishing fraudulent transactions from legitimate ones. Performance analysis was further supported through ROC curve visualization and classification result analysis. The results demonstrate that the proposed framework can effectively detect suspicious financial activities while maintaining efficient computational performance. The study provides a practical approach for building intelligent fraud detection systems in modern financial environments.
Hybrid CNN–Vision Transformer Framework for Human Metapneumovirus Detection: A Comparative Study of Deep Learning Models
Human metapneumovirus (hMPV) is a respiratory virus that can cause serious infections in children, elderly individuals, and patients with weak immune systems. Early detection is important because the symptoms of hMPV often resemble other respiratory diseases, making clinical diagnosis difficult. Artificial intelligence has recently become a useful tool for analyzing medical images and supporting disease detection. This study presents a comparative analysis of deep learning techniques used for the identification of hMPV from medical imaging data. Instead of training a new dataset, the research analyzes experimental results reported in previous studies and evaluates the effectiveness of different deep learning models. Traditional Convolutional Neural Network (CNN) models are first examined as the baseline approach for image classification. However, CNN models mainly capture local spatial features and may miss global relationships in medical images. To address this limitation, the study proposes a hybrid deep learning framework that combines CNN with a Vision Transformer architecture. In this framework, CNN layers perform local feature extraction, while the Vision Transformer module captures global contextual information through an attention mechanism. Performance values are reviewed from published research papers and compared using evaluation metrics such as accuracy, precision, recall, and F1-score. The analysis shows that hybrid CNN–Transformer models can achieve detection accuracies of approximately 91–94%, which is higher than conventional CNN approaches. These results highlight the potential of hybrid deep learning architectures for improving AI-assisted respiratory disease diagnosis.
Artificial Intelligence–Driven Functional Safety Approaches for Autonomous Vehicles
Autonomous vehicles are revolutionizing the modern world of transportation by the introduction of disruptive intelligent systems capable of perceiving the environment, making decisions and controlling the behavior of the transport unit with minimal participation by human drivers. However, the challenge of ensuring the safety and reliability of such systems is a critical one because autonomous driving is closely dependent on data-driven algorithms. Artificial intelligence techniques, and specifically machine learning and deep learning, have shown great potential to assist safety-related functions like object detection, environmental perception and risk assessment. Despite these advantages comes concerns of model transparency, reliability and if the AI will be compliant with established safety requirements set for automotive systems. This study discusses the role of artificial intelligence in enhancing safety mechanism of autonomous vehicles while taking into consideration of the functional safety regulations that are in place. A review of past studies involving artificial intelligence-based perception and decision-making models is carried out along with an experimental assessment in a simulated classification setting representing safety-critical and regular driving conditions. The performance of some of the AI models is measured by commonly used evaluation measures such as accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve. Experimental results show that AI-based models show successful differentiation of critical situations from normal operation with high classification performance. The findings indicate that artificial intelligence can play a huge role in improving the safety capabilities of autonomous vehicle systems where proper validation strategies and safety frameworks are implemented. However, challenges around explainability, computational efficiency and regulatory certification to ensure deployment of the AI-driven autonomous driving technologies are facing.
The landscape of Digital Nudging Research: A Bibliometric Review of Gaps, Emerging Themes, and Future Research Directions
Introduction: Digital technologies are shaping decision-making more and more in areas like banking, retail, healthcare, and public services. As an answer to the challenges of complicated choice environments which may lead to cognitive overload, digital nudging has arisen as a behavioural intervention designed to alter user decision-making in predictable ways through subtle changes in the interface design without restricting freedom of choice. Theoretical integration and cumulative knowledge development are challenges, as research on digital nudging is scarce and dispersed in different scholarly disciplines despite an increasing interest. Objectives: The purpose of this study is to systematically map the intellectual structure and evolution, identify key themes and influential contributors in digital nudging research, as well as to explore critical research gaps for future scholarly inquiry. Methods: The research conducts a bibliometric and conceptual analysis of 605 publications dated from 2002 to 2025 based on the Web Science database. The analysis uses Biblioshiny from R and includes descriptive performance indicators such as publications, authorship patterns, co-word analysis of thematic mapping and collaboration analysis to investigate publication trends, thematic structure, and intellectual origins of the field. Results: The results show that digital nudging is an emerging and multidisciplinary area of research, where behavioral economics, information systems and marketing contribute predominantly. Behaviour, information, and intervention effect are core themes; emerging areas like ethics, privacy and trust remain underdeveloped. Existing work is heavily biased toward low-dimensional, short-term experimental designs with relatively few longitudinal studies and little consideration of the underlying psychological mechanisms. In addition, gaps related to AI-driven nudging, cultural and institutional contexts of AI, and governance frameworks are identified. Conclusions: Almost all studies focus on specific applications of digital nudging (e.g., notifications, social norms) and thus lack theoretical perspective, as it tries to identify underlying mechanisms taking place in multiple settings based on the same theoretical framework (Fischer et al., 2020), while utilizing diverse ways of informing users. This study contributes to developing a fresh, integrated, ethically well-founded, and methodologically sound research agenda by identifying seven main Research Gaps and twenty-eight future research questions. The results are helpful for researchers, practitioners and policymakers who need to design responsible and effective digital interventions.
AI-Native Business Intelligence for Form-Driven Loan Origination Systems: A Cost-Efficient Architecture Using API Integration and Webhook-Driven Analytics
Loan origination systems (LOS), including commercial platforms such as Encompass and custom enterprise implementations, are architecturally form-driven: their data is organized within rigid field-based schemas that are largely inaccessible to conventional business intelligence tooling without costly middleware, proprietary connectors, or enterprise BI licensing agreements. This paper proposes the AI-Native BI (ANBI) framework — a novel architecture for delivering analytics, dashboards, key performance indicators (KPIs), and export capabilities directly from LOS data without reliance on third-party BI platforms. The ANBI framework exposes LOS data through standard GET API endpoints and webhook integrations, applies a schema-agnostic field mapping and calculation normalization layer, and stores consolidated data in a single-table structure optimized for AI-assisted analytics development. AI coding tools including Cursor and Claude are used to implement the BI layer, significantly reducing development time and enabling continuous iterative improvement. The framework eliminates enterprise BI licensing costs — estimated at approximately $24,000 annually for Power BI at organizational scale — while delivering equivalent or superior analytical coverage through AI-native development patterns. The architecture draws on foundational work in AI-integrated BI systems (Pathoori, 2025) and extends it to the domain-specific constraints of regulated financial origination environments. Results demonstrate measurable reductions in implementation cost, time-to-dashboard, and operational dependency on proprietary analytics vendors.
Dynamic Stability of Electric Vehicles under Slippery Conditions: Contribution of Fuzzy Logic in Anti-Slip Control
Introduction: In electric traction systems, the efficient conversion of electrical energy into mechanical energy is a key requirement for ensuring vehicle propulsion. This process relies on a powertrain composed of essential components such as electric motors, mechanical transmissions, and wheels in direct contact with the road surface. However, these systems are subject to significant mechanical disturbances, particularly the loss of wheel adhesion, which can adversely affect both vehicle stability and overall performance. To overcome this challenge, the development of robust control strategies is crucial, including advanced observation and regulation techniques. In this context, intelligent approaches such as fuzzy logic control offer a promising solution to enhance system performance under varying adhesion conditions. Objectives: The objective of this study is to investigate the application of a fuzzy logic-based control strategy for anti-slip regulation in electric vehicle traction systems, aiming to improve vehicle stability and performance under low-adhesion conditions. Methods: The methodology adopted for this research comprises the development of a fuzzy logic-based control strategy to mitigate wheel slip. A simulation model of an electric powertrain was implemented to evaluate the controller's response to abrupt changes in road conditions. Data collection was carried out through simulation results analysis. Results: The results revealed that the fuzzy logic controller stabilized wheel speeds and mitigated slip across varying road conditions. These outcomes confirm the strategy’s effectiveness, with limitations noted under extreme slip conditions, as discussed within the theoretical framework. Conclusions: This work demonstrates the effectiveness of a fuzzy logic-based anti-slip control strategy for electric traction systems. Simulation results confirm that the proposed approach successfully regulates the rotational speeds of the driven wheels and compensates for disturbances caused by adhesion loss. By maintaining vehicle stability even under adverse conditions such as wet or icy roads, this method proves to be both reliable and robust. Therefore, the application of fuzzy logic control presents a valuable perspective for improving the safety and performance of electric vehicles.
Influence of Physical Parameters on p-CuO/n-CdS/n-Zn2SnO4 Solar cell: A Numerical Investigation Achieving Efficiency of 28.50 % Using SCAPS-1D
This work presents a comprehensive numerical investigation of CuO thin-film solar cells using SCAPS-1D, focusing on replacing conventional CdTe and CIGS absorber layers with environmentally friendly alternative CuO metal oxide. The simulation explores the influence of absorber, buffer and window layer thicknesses, as well as temperature on the key photovoltaic parameters of CuO-based devices. The optimization study shows that an absorber thickness of 4 µm maximizes light absorption without excessive recombination, while the buffer and window layers achieve optimal performance at 0.02 µm. The results indicate that increasing the temperature from 300 K to 400 K causes a gradual decline in efficiency from 21.62 % to 16.58 %. Furthermore, specific parameters of p-CuO/n-CdS/n-Zn2SnO4 heterostructure, such as the influence of defects density, acceptor concentration, metal work function, Rs and Rsh were examined. Our findings revealed that the efficiency could be improved by increasing the acceptor concentration of p-type CuO layer and metal work function of the contact and reduced defects density and balancing between Rs and Rsh. The optimized p-CuO/n-CdS/n-Zn2SnO4 configuration attains a high conversion efficiency of 28.50 % under AM 1.5 illumination, demonstrating that CuO solar cell can achieve competitive performance. This study provides valuable insights for the design of cost-effective, non-toxic, and high-efficiency photovoltaic devices.
Knowledge Mapping and Evolution of Research on Curriculum Reform in Design Education: A Bibliometric Study (2006–2026)
Against the backdrop of global higher education transformation and the restructuring of paradigms in the design industry, curriculum reform in design education has evolved from localized exploration into an important branch of design research. This study aims to systematically examine the knowledge structure, research hotspots, and thematic evolution of this field over the past two decades, providing theoretical support for future teaching practice.Using the Web of Science Core Collection as the data source, this study retrieved and screened 668 core publications published between 2006 and 2026. CiteSpace (6.2.R4) was employed to conduct analyses of co-cited references, keyword co-occurrence and clustering, timeline mapping, and burst detection, thereby constructing a visualized knowledge map of the field.The results show that: (1) both the number of publications and citation frequency exhibit a continuous upward trend, entering a period of rapid growth after 2019, reflecting the high sensitivity of design education reform to socioeconomic transformation; (2) core research is mainly concentrated in the fields of architecture and education research, forming a stable knowledge base supported by professional competency assessment, systematic methodologies, and empirical case studies. The United States dominates research output, while China shows strong growth momentum as a second-tier contributor; (3) research themes have evolved from early focuses on basic skill instruction and studio-based models, to sustainable design and environmental responsibility, and further to digitalization, intelligent technologies, and student-centered approaches; and (4) burst detection indicates that Artificial Intelligence (AI), Virtual Reality (VR), educational equity, and interdisciplinary integration represent the current and emerging research frontiers.This study outlines the evolutionary trajectory of design curriculum reform research and identifies the dual driving forces of industry–education integration and intelligent teaching, providing references for future research directions and curriculum system optimization.
The Productive Mindset: A Conceptual Framework for Sustainable Productivity Through Awareness, Investment, and Motivation
Modern productivity research increasingly recognises that performance in knowledge economies cannot be explained solely through behavioural output metrics. Contemporary work environments require sustained attention, emotional regulation, and intrinsic engagement with complex tasks. This paper proposes the AIM Framework—Awareness, Investment, and Motivation—as a conceptual model explaining how psychological processes generate sustainable productivity. Awareness refers to metacognitive understanding of emotional and cognitive states. Investment refers to the strategic allocation of attention and psychological energy. Motivation represents intrinsic drivers that sustain long-term engagement. The framework distinguishes between maladaptive productivity states termed Productivity Blockers and adaptive states described as Productivity Champions. By integrating insights from organisational psychology, cognitive science, and behavioural research, the AIM Framework provides a human‑centred perspective on productivity in modern knowledge economies.
A conceptual framework for AI Maturity Assessment in Technology Supply Chains
Artificial Intelligence (AI) has become a fundamental part of technological supply chains which now use AI for better demand prediction, fraud prevention, risk assessment and efficiency improvement. Organizations face a problem with AI assessment because they currently operate in multiple supply chain environments yet need an assessment method which lacks standardization and specific industry guidelines. The current AI maturity frameworks focus only on business organizations which makes them unsuitable for measuring technology supply chain systems because they fail to account for dependencies and data exchange and system compatibility issues. The research introduces a new conceptual framework which enables organizations to assess their AI maturity through a dedicated framework for technology supply chains. The framework introduces a multi-dimensional structure which includes data readiness and model capability and infrastructure scalability and governance and ethics and interoperability and risk and resilience and organizational capability. The evaluation process assesses these dimensions through a five-level maturity continuum which starts from basic adoption and ends with complete automated AI implementation. The study establishes a complete assessment system which includes measurable performance indicators and assessment scoring system to conduct structured evaluations and performance comparisons. The framework becomes more understandable through its demonstration in real-world financial technology applications and cloud ecosystem operations and healthcare data systems. The implementation guide provides organizations with a step-by-step process to develop their artificial intelligence capabilities while reducing operational and systemic dangers. The research introduces a scalable assessment tool which assesses AI maturity in technology supply chains to benefit researchers and industry professionals. The proposed framework addresses a critical gap in current literature and provides a foundation for future empirical validation and industry standardization
Study of Financial Consolidation Maturity using OneStream and Oracle Cloud FCCS: Challenges, Benefits, and Future Directions
This review explores how cloud-based financial consolidation systems, specifically Oracle Cloud Financial Consolidation and Close (FCCS) and OneStream will transform the multi-entity enterprises. As the global operations and regulatory demands become more complicated, the conventional approaches to financial consolidation have become ineffective, erroneous, and sluggish. Cloud services on the other hand, streamline the process of key consolidation, including intercompany eliminations, currency conversions and journal entries, improving both accuracy, speed and compliance. Such systems have the capability to provide real-time financial reporting that will allow decision-makers to have access to real-time financial information to make better strategic decisions. The review identifies the main value of the cloud-based solutions, such as automation, compliance with the regulations, scalability, and real-time decision-making. It also deals with the issues of implementation including system integration, security and user adoption of the system. In addition, the review discusses the new trends in financial consolidation, including the incorporation of artificial intelligence (AI) into predictive analytics and the use of blockchain to improve transparency and security. Using practical case studies in various industries such as manufacturing, retail, healthcare, financial services, and technology the review illustrates how financial consolidation and reporting can and has been effectively simplified with the help of cloud based solutions. The further development of the financial consolidation is under the constant improvement of the cloud platform, which is more efficient, compliant, and capable of making decisions.
Context Engineering for Enterprise AI: Architecting Persistent, Governed Intelligence in Regulated Industries
Large language models demonstrate powerful reasoning capabilities across diverse domains. Enterprise deployments face persistent challenges when scaling beyond isolated use cases. Most implementations fail due to inadequate context management and governance frameworks. Traditional prompt-centric designs treat models as stateless reasoning engines without persistent memory. Each interaction starts fresh without access to historical decisions or institutional knowledge. Regulated industries require explainability, auditability, and jurisdiction-aware compliance. Current architectures lack systematic mechanisms to enforce these requirements. This article introduces Context Engineering as a foundational architectural discipline for enterprise artificial intelligence systems. Context becomes a first-class system component rather than prompt-level input data. The framework integrates structured and unstructured enterprise data through controlled memory stores and permissioned pipelines. Governance mechanisms are embedded directly into context construction and validation processes. Compliance rules operate proactively during context assembly rather than through reactive validation. Enterprise insurance implementations demonstrate improved decision consistency and reduced hallucination rates. The architecture enables scalable human-artificial intelligence collaboration while maintaining regulatory compliance. Intelligence emerges as an engineered system property grounded in persistent context rather than solely as model capability.
Resilient Self-Service Search Architecture with a Dedicated Fallback Index
Self-service search is typically a user's first interaction with support content. It translates an end-user's informal, noisy query text into a support answer. However, due to bursty search traffic, rapidly evolving content, and expensive retrieval features, it is challenging to maintain the online reliability of such web-scale search systems. The system is made up of several layers that can handle a lot of searches, including a main part that spreads out the search load and a backup index that helps manage problems and keeps response times steady. Routing is done via health checks and circuit breaker design patterns, allowing for graceful degradation in high load and partial failures. Bounded query normalization, field-weighted document modeling, and tiered empty-result avoidance make degraded mode possible and useful. Scheduled drills, versioned index lifecycle management, and user-outcome observability keep the fallback warm as content and traffic evolve. All these enable a dedicated fallback index as a first-class reliability mechanism, instead of something that is only ever invoked in emergencies after the fact.
Structural Foundations of Governance-Centered Enterprise Case Processing Architecture
Enterprise case processing platforms in compliance-driven environments face a challenge that goes well beyond technical performance. These systems must guarantee procedural legitimacy, institutional accountability, and defensible outcomes—properties that conventional enterprise architectures are simply not built to provide. This article develops a formalized architectural framework that treats governance not as a procedural overlay but as something woven into the structure of the system itself. Rather than relying on supervision and post-event auditing to catch problems after they occur, the framework encodes institutional mandates directly into computational execution logic. Four structural components anchor the framework: deterministic lifecycle modeling through finite state machines, hierarchical authorization enforcement aligned with institutional authority, cryptographically verifiable append-only audit logging, and distributed coordination mechanisms that preserve ordered event processing. Drawing from institutional theory, socio-technical systems research, and secure systems engineering, this work positions governance-centered architecture as a compliance-deterministic design doctrine—one that repositions enterprise case processing platforms from administrative utilities into genuine institutional enforcement infrastructure.
Compliance-Aware AI Deployment Architectures for Enterprise Financial Decision Platforms
Financial institutions are adopting artificial intelligence (AI) to support decisions in areas such as risk assessment, customer recommendations, fraud detection, and operational automation. However, deploying AI in regulated environments is more difficult than deploying conventional software. AI systems can change in performance over time, depend heavily on data quality, and create added demands for explainability, traceability, and governance. As a result, financial institutions need deployment architectures that treat compliance, auditability, and resilience as built-in design requirements rather than after-the-fact controls. This article presents a compliance-aware AI deployment architecture for enterprise financial decision platforms. The proposed approach combines decision orchestration, policy-based control, model version management, approval workflows, monitoring, rollback, fallback mechanisms, and evidence capture. The architecture is designed to help organizations manage AI deployment in a controlled way while preserving accountability and operational continuity. The analysis shows that successful AI deployment in regulated financial environments depends not only on model accuracy but also on the surrounding production architecture. Explainability must be supported by decision-time metadata and traceable workflows, auditability must be enabled through versioned and reviewable deployment records, and resilience must be provided through monitoring and safe fallback paths. The resulting framework offers a practical foundation for scaling AI-enabled decision systems while maintaining regulatory compliance, operational reliability, and institutional trust.
Enterprise Framework for Standardizing Platform Engineering Across Multi-Cloud Environments: Architectural Harmonization of Amazon Web Services and Microsoft Azure
Multi-cloud strategy adoption has introduced significant architectural fragmentation across enterprise platform engineering teams. While organizations leverage multiple cloud providers for resilience and vendor lock-in avoidance, divergent identity models, networking abstractions, governance mechanisms, and deployment constructs create operational complexity and inconsistent security postures. Existing literature emphasizes cloud comparison and workload portability, with limited focus on systematic platform engineering harmonization. This article presents a governance-driven enterprise multi-cloud platform harmonization framework standardizing platform engineering practices across heterogeneous cloud environments. The framework addresses architectural abstraction alignment, identity federation normalization, network segmentation consistency, and deployment pipeline portability. Implementation across Kubernetes-based platforms in Amazon Web Services and Microsoft Azure incorporated Infrastructure-as-Code automation, identity equivalence mapping, and policy-driven governance controls. The framework introduces the Deployment Consistency Index, Identity Alignment Score, and Governance Conformance Ratio to quantify standardization outcomes. Empirical evaluation demonstrates reduced configuration variance, improved deployment reproducibility, minimized cloud-specific policy exceptions, and enhanced governance traceability. This work contributes a reusable architectural blueprint for multi-cloud platform standardization with empirical evidence supporting governance-centered harmonization strategies.
Design Systems Architecture for Scalable Enterprise Platforms
Enterprise analytics platforms across telecommunications, financial services, healthcare, and infrastructure domains increasingly face challenges in maintaining consistency, interpretability, and scalability as they serve diverse user populations and data sources. Traditional design systems work well for consumer apps but fall short for enterprise analytics because they can't handle the complex meanings, performance demands, and governance needs that come with data-heavy platforms. The proposed seven-layer architectural framework treats design systems as socio-technical infrastructure, integrating human-centered design principles with systems engineering practices. The architecture includes basic layers for design tokens and main components, specific layers for data elements and visualization tools, layers for ensuring clear semantics and workflow patterns, and governance systems for lasting effectiveness. This approach demonstrates its real-life applicability in business intelligence tools, network monitoring systems, and financial dashboards, addressing technical challenges such as inter-connecting different systems and improving performance. Additionally, it offers organizational benefits, including enhanced stakeholder alignment, reduced development redundancy, and increased user trust through consistent interpretive frameworks. The layered architecture makes it easier to handle complexity in a systematic way, supporting scalable and comprehensible analytics experiences in complex business environments.
Agentic AI for Commercial Decision Intelligence in Retail & CPG
The retail and consumer packaged goods industries are at an inflection point; the autonomous, goal-oriented software agents are substituting the inflexible, analyst-reliant business decision cycles with closed-loop intelligence systems, which can perceive, reason, and act in real-time. The autonomy, proactivity, and constant learning of agentic AI redesign the pricing, trade promotion optimization, and supply chain coordination processes within complicated, multi-account business settings. Based on proven sources of empirical evidence in the literature on machine learning, multi-agent reinforcement learning, and supply chain optimization, the technical architecture of an agentic commercial system is discussed along five related dimensions: autonomous trade performance monitoring through perception-reasoning-action pipelines; cooperative multi-agent system design under the models of centralized training and decentralized execution; scenario simulation engine based on digital twin models; multi-objective trade promotion optimization with Pareto-front metaheuristic algorithms; and practical barriers of data infrastructure, model drift, organizational change management, and algorithmic governance. Bringing these capabilities together into a single agentic decision stack is a paradigm shift in the concept of commercial intelligence in retail and CPG, moving the operational center of gravity off retrospective dashboards and onto adaptive, constantly learning systems that coordinate the decisions on pricing, promotion, and supply.
Impact of Iterative Decoding Techniques on the Reliability of Communications in Optical Transmission
With the fast-paced advancement of communication technologies, there is an increasing demand for higher bandwidth, stronger reliability, and greater resilience. Although optical transmission offers excellent efficiency, it is still prone to certain performance issues. This paper investigates how iterative decoding methods—especially turbo codes and LDPC codes—can enhance the reliability of optical communication systems. It outlines the basic concepts, highlights the strengths and limitations of each technique, and discusses future directions. A comparative simulation is also included to demonstrate their practical effectiveness.
AI-Driven Real-Time Data Quality Validation in Healthcare ETL Pipelines
Pressure on healthcare data pipelines is growing to provide accurate, consistent, and regulatory-compliant data in real-time; traditional extract-transform-load validation models are still rooted in the concept of ensuring that data is correct, consistent, and regulatory-compliant but fail structurally to meet the velocity and complexity of current healthcare data environments. Smart, self-evolving validation built into streaming ETL processes is an architectural breakthrough that enables data quality testing to become more than a reactionary activity after the data has loaded; instead, it's a capacity that executes while the system is operational. With machine learning-based anomaly detection, such as Isolation Forest, autoencoder neural networks, and statistical modeling and schema drift monitoring and threshold adaptation reinforced by reinforcement learning, ETL pipelines can have the ability to detect and intervene in data integrity failures before they escalate into downstream clinical, financial, and regulatory systems. Explainable AI systems make sure that each automated quality decision is supported by a mode of interpretation, meeting the traceability and auditability standards that the healthcare regulatory frameworks have established regarding the protected health information. Unalterable audit logging transforms compliance records from a periodical manual process into a pipeline property that is run on an automatic basis. Automated correction, quarantine, and intelligent reprocessing of anomalous records are all possible with self-healing remediation capabilities without interrupting the pipeline and the manual intervention burden inherent to the traditional quality assurance models. These intelligent validation capabilities can remain sustained at enterprise volumes of healthcare data without placing throughput pressure on distributed computing architectures that have the required horizontal scaling. The combination of all these capabilities creates an ETL infrastructure that proactively protects the integrity of data instead of just passively accepting records, which creates a reliable basis of data to make clinical decisions, model population health, operate the revenue cycle, and provide regulatory reporting.
Governing Trustworthy Generative AI in Enterprise Product Ecosystems
Generative artificial intelligence is rapidly becoming a foundational capability within enterprise product ecosystems, enabling new forms of automation, personalization, and decision support. However, its probabilistic behavior, opacity, and deep integration across interconnected products introduce significant trust, risk, and interpretability challenges. This study examines how trustworthy generative AI can be effectively governed in enterprise product ecosystems by adopting an ecosystem-level, socio-technical perspective. Using a mixed-method research design that integrates conceptual modeling, qualitative investigation, and quantitative analysis, the study evaluates the influence of key governance dimensions including transparency, data governance, accountability, model lifecycle management, human-in-the-loop oversight, and regulatory alignment, on trustworthiness outcomes. The results demonstrate that transparency and data governance are the strongest determinants of trust, while ecosystem complexity moderates governance effectiveness through propagation effects across interconnected products. The findings highlight that governance mechanisms operate synergistically rather than independently and must scale systematically with ecosystem complexity. The study contributes a structured understanding of generative AI governance as a strategic enterprise capability that supports responsible scaling, stakeholder confidence, and sustainable innovation in complex product ecosystems.
Taxonomy of Transport Layer Security (TLS) Revocation Failure Scenarios
Certificate revocation is one of the most operationally fragile components of the Transport Layer Security ecosystem. While TLS provides robust cryptographic guarantees for data in transit, those guarantees depend on the continued validity of the certificates underpinning them. When revocation mechanisms fail, trust decisions become unreliable, and the security posture of TLS-secured communications degrades in ways that are often invisible to the relying party. Existing discussions of revocation failure tend to conflate the underlying failure conditions with the validation responses they produce, obscuring the structural causes of trust breakdown and limiting the precision of incident analysis. This article addresses that gap by proposing a formal taxonomy of TLS revocation failure scenarios, classifying failures along four orthogonal and implementation-agnostic dimensions: failure origin, temporal validity of revocation signals, scope of impact, and trust determinism. Each dimension captures a distinct aspect of revocation failure behavior that cannot be derived from the others. The taxonomy is designed to remain applicable across the full diversity of TLS deployment environments, from constrained embedded systems and IoT infrastructure to large-scale public web deployments and intermediary-mediated architectures. If failure classification is separated from validation behavior, then the taxonomy can provide a neutral analytical foundation for reasoning about revocation reliability, which may support a deeper understanding of trust behavior under failure. It can further lay groundwork for future architectural and operational analysis.
Modular Orchestration Engine for Event-Driven Chargeback Processing: Transforming Sequential Dispute Resolution into Parallelized Workflows
For many years, the way payment processing systems handle chargebacks has followed a traditional, step-by-step method that creates delays, compliance risks, and inflexibility. In a world of increasing digital payment volumes and growing consumer familiarity with the right to dispute related to payment transactions, these challenges translate to quantifiable exposure and inferior processing efficiency. This article explains a flexible system for handling chargeback disputes using a modern setup that reacts to events, which uses clearly defined rules to automatically manage different steps in the chargeback process at the same time. The engine also includes a publish-subscribe event bus, a rule evaluation layer based on directed acyclic graphs, and an action execution layer that utilizes circuit breakers and adaptive backpressure. The solution includes a visual workflow configuration user interface that allows dispute operations specialists to change decisioning logic without developer intervention, simulation tools, and role-based governance. This setup allows for faster processing, better handling of delays, flexible scaling during busy times, and better meeting of deadlines, helping financial institutions respond more quickly to changing rules, competition, and customer needs in payments.
Attentional Governance in Human-AI Decision Systems: A Technical Architecture for Judgment Integrity
The adoption of artificial intelligence systems has changed how decisions are made in organizations to facilitate interactions between complex ecosystems that determine the generation of informational landscapes by the outputs of algorithms as well as how such landscapes influence judgment that is affected by humans. As an alternative to the human decision-makers, the contemporary AI architectures bring about basic alterations in the circumstances of attention in which the verdict is provided. The traditional systems of governance that are centered on allocation of power and accountability mechanisms have not considered the significance of attention as a determinant of decision integrity. This journal advocates attentional governance as a socio-technical notion, and inattention within good governance ought to be an architectural design that deals with cognitive processes of involvement and not acts of procedural compliance. Such modes of attentional failure as automation bias, information overload, attentional erosion, and signal convergence are outlined in the framework as systematic weaknesses in the human-AI decision system. It is proposed to counteract those weaknesses with five structural mechanisms, which are interpretive checkpoints, which the deliberation process should have explicit and articulate reasons; attention pacing to enable deliberation intervals; triggering escalation to route complex decisions through lengthy deliberation, override rights to be able to go outside of the algorithm advice, and traceability mechanisms to document decision composition. It involves certain leadership capabilities, which are a synthesis of organizational, technical, and cognitive knowledge to be implemented. The organizations must be conscious of the fact that nominal human authority without the power of attention will create the illusion of governance whereby the decision-makers are ratifying systems. The framework provides the leadership in technology with feasible design considerations that will strive to preserve the integrity of judgment at the organizational level and manage the hybrid human-AI teams operating in the unpredictable and complex settings.
Algerian Municipalities: Reality of Administrative Organization, Participation of Elected, and Citizens' Challenges of Governance Case of Algiers Municipalities
Introduction: The province of Algiers faces mounting administrative and governance challenges linked to rapid urbanisation, spatial transformation, and the limited performance of local elected officials. Objectives: To evaluate the organisational structures and framing of Algiers municipalities, assess the participation of elected officials and citizens in local governance, and identify the obstacles to implementing good governance standards in the capital. Methods: The study combines analysis of official data from the National Office of Statistics with fieldwork comprising closed questionnaires and personal interviews distributed to 52 elected officials and 132 citizens across central and suburban municipalities in Algiers. Results: Municipalities suffer from inconsistent organisational structures, low framing ratios (averaging 8.53%), and a lack of training for elected officials. Nearly half of elected officials do not participate in decision-making, and citizen participation in urban affairs remains weak at 24.24%, reflecting a significant gap in governance standards. Conclusions: Algiers requires a special legal framework tailored to its metropolitan status, revised organisational structures, enhanced capacity-building for elected officials, and effective mechanisms for citizen and civil society participation to achieve good local governance.
AMLGTTA: Adaptive Multicloud Load Balancing by Harnessing GAHP and TLGWO for Enhancing Task Allocation
The study introduces a multicloud load balancing approach, harnessing Genetic Analytical Hierarchical Processing (GAHP), an extension of AHP. This adapts its internal weights dynamically to reclassify VMs in response to fluctuating demands. For efficient task allocation, tasks are clustered, considering parameters such as makespan, deadline, resource utilization, and notably dependency levels, with dependency accorded utmost priority. Task allocation to VM categories is facilitated using the Teacher Learner-based Grey Wolf Optimizer (TLGWO). Additionally, task parameters are encrypted using specially designed Intelligent Physical Unclonable Functions (IPuFs) to ensure secure data transmission to respective VM Category Groups (VMCGs). Within VM category groups, task assignments are optimized using the local TLGWO model. The proposed model demonstrates significant advancements over existing frameworks. Specifically, a 9.5% reduction in makespan, 4.9% enhancement in VM computation efficiency, 2.5% improvement in deadline adherence, 4.5% increase in task diversity, 3.9% boost in execution efficiency, and 3.4% decrease in decision latency. This model has shown to decline in inter-cloud communication delay by 2.5%, elevate throughput by 1.9%, and enhance packet delivery performance by 2.4%. Consequently, our proposed multicloud load balancing model furnishes a robust solution, mitigating extant constraints and ensuring heightened performance and efficiency in multicloud scenarios.
Partnership Strategy of Large-Scale Enterprises and Small - Medium Enterprises In Increasing National Economic Competitiveness
Purpose- This study aims to analyse the strategic role of partnerships between large-scale enterprises and small-medium enterprises (SMEs) in enhancing national economic competitiveness. It explores how partnership patterns, access to technology and information, and market access contribute to strengthening SME capacity, which in turn mediates their impact on economic performance. Design/Methodology/Approach- A quantitative approach is used with the path analysis method. Data was collected from micro, small, and medium enterprises in the Banten Province and DKI Jakarta Indonesia areas. The analysis focuses on the direct and mediated effects of partnership strategy variables on the competitiveness of the national economy. Findings- This finding is strengthened by the results of a mediation test that shows that the capacity of SMEs is able to effectively mediate the influence of these three variables on national competitiveness. The results of the research are in line with business partnership theory, resource-based view, market access theory, and competitive advantage theory in the context of strengthening inclusive economic structure Originality/Value- This study contributes to the literature by integrating theories of business partnership, resource-based view, market access, and competitive advantage in the context of inclusive economic development. The research underscores the strategic importance that collaboration between large business actors and SMEs with the support of government policies can create productive partnerships and can encourage national economic growth.
An Advanced Lateral Control System Based on Multi-Machine Control for a Four-Wheel-Drive Electric Vehicle
Introduction: Traditional mechanical differentials in four-wheel-drive (4WD) vehicles distribute torque passively and symmetrically, which limits their ability to adapt to varying road conditions. This limitation negatively affects lateral stability and vehicle maneuverability, especially during high-speed cornering and low-traction scenarios. Objectives: This study aims to develop an advanced lateral control strategy for 4WD electric vehicles by replacing the conventional mechanical differential with a controllable electric differential, in order to enhance vehicle stability, handling, and safety. Methods: The proposed approach is based on multi-machine control, where each wheel is driven by an independent electric motor supplied by two synchronized three-level Neutral Point Clamped (NPC) inverters. This configuration enables precise and real-time torque distribution among the wheels. An electric differential control algorithm is implemented to actively regulate wheel torques, ensuring proper yaw rate and lateral force control under varying driving conditions. Results: The effectiveness of the proposed system is validated through numerical simulations under critical driving conditions, including sudden steering inputs and low-adhesion surfaces. The results demonstrate improved lateral stability, reduced trajectory deviation, and strong robustness of the control strategy. Conclusions: The study confirms that electric differential-based control is a promising solution for next-generation electric vehicles. It significantly enhances vehicle handling, agility, and safety by enabling adaptive and intelligent torque distribution.
Copyright (c) 2026 Journal of Information Systems Engineering and Management
The Effect of Corporate Social Responsibility Disclosure, Profitability, and Firm Size on Firm Value: The Moderating Role of Environmental Performance
Firm value is an important indicator of corporate success in creating wealth for shareholders and other stakeholders. In addition to financial performance, firm value is also influenced by non-financial factors such as Corporate Social Responsibility (CSR) disclosure and environmental responsibility. This study aims to examine the effect of CSR disclosure, profitability, and firm size on firm value and to analyze the moderating role of environmental performance in these relationships. This research employs a quantitative approach using secondary data obtained from companies’ annual reports and sustainability reports during the observation period. The data are analyzed using panel data regression and Moderated Regression Analysis (MRA). The results show that CSR disclosure and profitability have a significant effect on firm value, while firm size does not have a significant direct effect. Environmental performance strengthens the relationship between CSR disclosure and firm value but does not strengthen the relationship between firm size and firm value and weakens the relationship between profitability and firm value. These findings indicate that firm value is influenced by the interaction between financial and non-financial factors within the framework of stakeholder theory.
The Impact of ESG on Local Socio-Economic Development: Based on the Practical Experience of Hai Phong City
This study analyzes the impact of environment, society, and governance on the socio-economic development of a locality through the case of Hai Phong city. Building upon stakeholder theory, sustainable development theory, and previous empirical studies, this paper constructs a research model with three independent variables: environment, society, and governance, and one dependent variable: the socio-economic development of the locality. The research results show that all scales achieve good reliability and measurability. The regression model is statistically significant, with all three factors-environment, society, and governance-having a positive impact on the socio-economic development of the locality. The results also indicate that governance is the factor with the strongest impact, followed by society, and finally the environment. Based on this, the article emphasizes the need to improve the quality of local governance, promote inclusive social policies, and strengthen the integration of environmental goals into Hai Phong's development strategy to achieve more sustainable, efficient, and resilient growth.
Factors Influencing Green Human Resource Management: A Study in the Vietnamese Coal Industry
In the context of sustainable development becoming an increasingly urgent requirement, green human resource management plays a crucial role for businesses operating in industries with high environmental impact. This study focuses on analyzing the factors influencing green human resource management in the Vietnamese coal industry. The results show that leadership commitment and a green organizational culture are the two factors with the strongest influence on the implementation of green human resource practices in enterprises. In addition, employees' environmental awareness and institutional pressure also have a positive impact, although to a lesser extent. Based on this, the study emphasizes the need to strengthen leadership, build an environmentally oriented organizational culture, and raise employee awareness to promote green human resource management, contributing to the green transition and sustainable development in the Vietnamese coal industry.
The Ethical and Regulatory Challenges of AI-Driven Financial Decision-Making in Global Markets
The integration of artificial intelligence (AI) into financial decision-making has fundamentally transformed global markets, introducing unprecedented efficiency alongside profound ethical and regulatory challenges. This paper presents a narrative review of the ethical and regulatory challenges arising from AI-driven financial decision-making. Drawing on recent literature (2020–2025) across computer science, law, finance, and public policy, it examines five intersecting dimensions: ethical frameworks, regulatory responses, algorithmic bias and explainability, governance architectures, and emerging policy directions. The analysis reveals that existing regulatory frameworks, designed for human decision-makers, remain inadequate for adaptive machine learning systems, while technical opacity continues to undermine fairness and accountability. The paper's main contribution is in pulling these areas together and identifying where they interact, particularly between lifecycle aligned regulation and multi layered governance, which prior reviews have largely treated separately. It offers policy recommendations for regulators, financial institutions, and technology developers, with a focus on cross border harmonization and domain specific regulatory design.
Large Language Models for Natural Language Processing: Architectures, Training Paradigms, and Real-World Applications – A Systematic Review
In this manuscript, Large Language Models (LLMs) are praised as a transformative paradigm in the scope of man-made insight, which invigorates tremendous improvement in the capacities of Natural Language Processing (NLP) mechanisms. The triumph of transformer architectures and large scale pre- training has lead to latest LLMs to consistently provide strong performance for a wide range of tasks - from generating text to question answering, translation and reasoning - thus demonstrating their true utility. The ability of these models for assimilation of intricate datasets through linguistic pattern recognition has led to a great deal of progress in both academic research and industrial practice. This investigation presents a holistic and systematic look at the recent scholarship on large language models, and it has a concentrated focus on the topics of architectural evolution, training paradigms, and real-world applications. A systematically structured literature search was carried out from the most prominent academic databases: IEEEXplore database, ACM Digital Library, Scopus, Science Direct and arXiv from 2023 to 2026. The study follows a methodology that is modelled on Preferred Reporting Items for Systematic Reviews and Meta,-Analyses (PRISMA) guidelines. Items for Systematic Reviews and Meta-Analyses (PRISMA), and included the use of well-defined inclusion and exclusion criteria to identify relevant peer reviewed articles and high impact preprints. The review explains important new developments in LLM architectures, which include improvements in transformer architectures, mixture-of-experts architectures, and new alternatives which are focused on efficient sequence modelling. It further challenges the paradigms of training, such as training from a large scale (pre-training), instruction tuning, parameter efficient fine tuning (e.g. LoRA), reinforcement learning from human feedback (RLHF). In addition to this, the paper highlights the growing use of LLMs in other areas such as healthcare, education, software, and scientific research. In spite of their successes, there are significant challenges remaining - hallucination, bias, computational cost and evaluation limitations. The open research problems and possible futures for efficient, reliable, and trustworthy large language models are identified, ending the review.
Architecting Event-Driven Enterprise Cloud Platforms for Scalable Cross-System Orchestration
Enterprise integration designs that use direct commands are becoming less effective in today's complicated digital world, where interconnected services can cause failures, slow deployments, and more operational problems. Event-driven architecture provides a better structure by replacing direct commands with a reliable and asynchronous Event-driven architecture provides a structured way to replace direct commands between systems with a reliable and delayed sharing of unchangeable updates. However, achieving its benefits requires deliberate architectural discipline, not just casual adoption. This article explores the basic principles, design factors, and rules for the governance that sets sustainable event-driven enterprise platforms apart from ad hoc messaging systems. It addresses domain-oriented event semantics, consumer design, schema governance, and the complementary roles of orchestration in managing distributed business processes. It further explores the scalability engineering, business-aware observability, and security controls that high-volume event platforms demand at enterprise scale. All these aspects together form a clear architectural plan for businesses that want to create integration systems that are strong, easy to track, and able to adapt as the organization and technology change.
Engineering Management of River Dredging for Flood Protection: An AI-Assisted Socio-Hydrodynamic Study of the Ghaghara-Saryu Basin, Uttar Pradesh, India
Riverbank erosion remains a persistent and serious threat to rural communities and agricultural land in the Gangetic plains of India, where highly active alluvial rivers cause continuous lateral migration and bank failure during monsoon seasons. This study evaluates the effectiveness of sectoral dredging as a soft-engineering river training approach through two field interventions in Uttar Pradesh: the Ghaghara River at Village Sirauli Gung, District Barabanki, and the Saryu River at Village Bahadurpur, District Gonda. Pilot channels, referred to as cunettes, measuring 3.8 km and 3.9 km in length and 45 metres in width, were excavated to redirect the river’s main current away from vulnerable concave banks. A hybrid bank stabilisation system combining Geotubes and Balli piling was deployed to induce siltation in abandoned reaches and reinforce the bank toe. Data acquisition integrated LiDAR surveys, high-resolution UAV photogrammetry, bathymetric mapping, and 2D hydrodynamic modelling using HEC-RAS to validate hydraulic performance and sediment transport behaviour. Discharge calculations based on Lacey’s Regime Theory confirmed that the standard 45-metre cunette can convey approximately 472.63 m³/s, while the widened 100-metre channel section achieved a capacity of approximately 1,458 m³/s. Post-intervention numerical simulations and field observations confirmed a significant reduction in near-bank flow velocity to 0.8–1.2 m/s and boundary shear stress to below 15 N/m². Economic evaluation yielded Benefit-Cost Ratios of 1.41:1 for the Ghaghara reach and 1.35:1 for the Saryu reach. In total, 13.46 lakh cubic metres of dredged material were reused in situ for embankment reinforcement and land reclamation, consistent with the National Framework for Sediment Management-2022. The interventions secured the livelihoods of approximately 22,000 residents, protected 8,000 hectares of fertile agricultural land, and generated 15,288 temporary employment days for local communities. These findings present sectoral dredging as a scalable, cost-effective, and ecologically responsible model for flood risk reduction in dynamic braided river systems.
Grounding LLM-Based Engineering Assistants in Re-al-World DevOps Discourse for CI/CD Workflows
Continuous Integration and Continuous Deployment (CI/CD) are central to modern DevOps, enabling teams to build, test, and deploy software quickly and reliably through automated pipelines. As these workflows grow more complex, developers increasingly turn to intelligent assistants for help diagnosing failures, understanding configuration logic, and resolving recurring pipeline issues. Although recent Large Language Models (LLMs) have shown strong performance on code-related tasks, they often struggle with mixed natural language–code reasoning and with grounding their answers in real-world DevOps scenarios. To address these gaps, we introduce DevOpsAssistant-SOD, an LLM-powered engineering assistant trained and evaluated on large-scale Stack Overflow data, comprising 218.5 million natural language–programming language pairs and 1.4 million labeled question pairs. The model leverages duplicate-aware contrastive learning to better capture contextual similarities across CI/CD-related questions and issues. Experimental results show that DevOpsAssistant-SOD improves failure diagnosis accuracy by 9.3%, duplicate issue detection F1 by 11.1%, and response relevance by 8.7% compared to strong general-purpose code LLM baselines.
Implementing Clinical-Grade Data Pipelines for AI-Driven Diagnostics Using HPC and High-Speed Fabrics
This paper has applied a modeling pipeline consisting of AI-based diagnostics made possible through the use of computing based on the principles of high-performance computing (HPC). This system receives, interprets and processes multi-modal healthcare information and data (ICU monitor streams, medical imaging and genomic data). It is also low-latency and heavily throughput with use of high-speed fabrics such as infinity band and NVlink in addition to processing ICU data (1,200 records/sec), imaging (350 GB/hour) and genomic data (420GB/hour). AI model outcomes are promising: having an ICU risk prediction F1-score of 0.76, EfficientNet-B0 of 99.5% internal and External imaging accuracy and 95% of predictions of genomic variations, XGBoost, EfficientNet-B0, and External imaging model are high. The system also ensures fault-tolerance, data integrity besides the scaling resource efficiency among the distributed nodes of HPC. The experience of integrating in NYU Hospitals and City of Hope increased the clinical process, decreasing the alert time in the ICUs to 40 per cent, clinician satisfaction increased to 4.5/5. The article indicates that at present HPC-capable AI pipelines are able to deliver reproducible, real-time and clinically actionable insights to offer precision healthcare.
Leveraging Artificial Intelligence to Combat Cyber Threats in Financial Institutions
The proposed study explores how artificial intelligence can be used to improve cybersecurity in financial institutions through machine learning, deep learning, and behavioural biometrics in the detection of fraud. It compares the models, including the Random Forest, Logistic Regression, Isolation Forest, and a deep-learning classifier. The mean reveals that Random Forest is better than Logistic Regression at an accuracy of 78% compared to 51% which is statistically significant (p = 3.37e-08). Nevertheless, none of the models can adequately detect fraudulent transactions indicating that it is quite challenging to identify fraud in real-time in financial ecosystems, and that AI-based detection systems still require further enhancements.
Building a Career in Cloud Engineering—From Automation to Architecture
Cloud engineering is now a foundational discipline that influences the development of the design, provision, and governance of digital infrastructure in modern organizations. The intersection of scripting and automation, infrastructure-as-code habits, containerization, and observability has offered a technical career ladder that carves a technical career ladder and requires specific tool expertise, as well as a tactical body of thought. Since automation-first scripting frameworks and version-controlled configuration management are initial steps, cloud engineering knowledge accumulates gradually with declarative infrastructure provisioning, container orchestration, CI/CD pipeline architecture, and platform engineering. These technical fields are accompanied by an increased emphasis of the profession on communication clarity, a culture of blameless incidents, organized certification strategies, and mentorship abilities. It is not the knowledge of any single tool that makes a career in cloud engineering; it is the skill to move through a larger ecosystem of mutually reinforcing fields that can be integrated and optimized into coherent and scalable platforms to benefit both the engineering team and organizational goals.
Data-Driven Strategy for Scaling Supply Chain Operations in Growth Enterprises
Growth enterprises face increasing supply chain complexity as they expand across markets, product lines, and operational networks. Traditional supply chain models often fail to scale efficiently under such conditions, leading to coordination inefficiencies, rising costs, and reduced responsiveness. This study examines how data-driven strategies enable the scaling of supply chain operations in growth enterprises by integrating data capabilities, analytics maturity, and decision automation into strategic supply chain management. Using quantitative research design, primary survey data and secondary operational metrics were analyzed through reliability testing, factor analysis, structural equation modeling, and regression techniques. The results demonstrate that data integration and analytics maturity significantly enhance supply chain agility, which acts as a key mechanism linking data-driven strategies to scalable performance outcomes. Decision automation further contributes directly to improvements in efficiency, responsiveness, and cost-to-serve during growth. Visual analyses reinforce these findings by illustrating both linear and interactive effects among key variables. The study concludes that a strategically aligned, data-driven approach is essential for achieving scalable, resilient, and high-performing supply chain operations in growth-oriented enterprises, offering valuable theoretical and managerial insights for sustainable expansion.
Digital Twin Frameworks for Predictive Risk Management in ERP Transformations
ERP transformations are among the most complex and failure-prone enterprise transformations in modern enterprise life. They consistently show patterns of investment overruns, schedule slippages, and scope shortfalls that earlier risk management frameworks have not been able to manage. The Digital Twin for ERP Transformation framework combines system-of-systems modeling, streaming telemetry, and machine learning-based predictive risk analytics with existing project risk management methodologies to create a dynamic computationally synchronized digital twin of the transformation's risk profile. Guided by ISO 23247 digital twin architecture, NIST AI RMF governance function, and PMBOK risk management processes, operationalize a four-layer digital twin reference architecture of observable program components, integration and telemetry, twin core models, and decision applications. Engineered leading indicators, including change-request entropy, defect acceleration index, backlog flow efficiency, and migration yield, are fed to machine learning classifiers and Monte Carlo simulation engines. This replaces milestone plans with honest, continuously updated uncertainty distributions, which are used to predict probabilistic schedule and cost risk curves. Pathway-specific calibration for greenfield, brownfield, and hybrid ERP migration strategies requires different risk profiles and governance requirements. A system of explainability, bias testing, and human-in-the-loop accountability structures for AI systems to meet AI trustworthiness principles is used to achieve governance obligations. The resulting program risk management framework changes the model of status reporting from after-the-fact to continuous, predictive, actionable intelligence for low-cost intervention before risk materializes, considerably lowering the risk of program failure in complex multi-phase ERP transformation projects.
APIs as Digital Supply Chains: An Architectural Framework for Enterprise Data Logistics
APIs are an essential mechanism for orchestrating flows of information across systems‚ organizations and countries‚ but although considerations for well-formed components are widely established‚ an architecture framework based on the concept of 'API-as-supply-chain' is still very much in its infancy․ Therefore‚ practitioners and researchers are currently unable to adapt the theory and applications of successful SCM constructs such as resilience‚ traceability‚ governance‚ and throughput optimization to the API context․ This article tries to address this gap by proposing a five-layer API infrastructure architecture that reflects the SCM logistics functions of sourcing & production‚ processing & transformation‚ distribution & delivery‚ experience aggregation‚ and governance & compliance․ The framework was developed according to a design science research methodology‚ and was validated through a formal expert review through the Analytic Hierarchy Process‚ with a consistency ratio of 0․044 by 7 domain experts․ The model applies established integration patterns and supply chain management theory․ Business ecosystems and monetization are horizontal concerns․ The paper contributes to the research of API ecosystems as a value logistics system‚ and provides systematic guidance to practitioners on how to design resilient‚ governable and scalable digital infrastructures․ Future areas of research include self-optimizing API ecosystems based on artificial intelligence (AI) and composable enterprise architecture․
Transforming Project Management: Evolving from Managed Delivery to Autonomous Execution in the Age of AI
The paper discusses how Artificial Intelligence (AI) can transform project management by changing traditional models of management from a delivered service to a fully autonomous execution system. The research mentions how AI can contribute to the project's success, including cost-effectiveness, time management, and resource optimization. With the help of quantitative analysis, the paper can review the advantages and limitations of AI utilization and its impact on the project success rates and decision-making outcomes. The results imply that project performance could greatly improve when AI is implemented, but issues like the initial investment expenses and organizational preparedness are still present.
Security and Resilience in Modern Distributed Commerce Architectures
Modern digital commerce platforms increasingly adopt distributed microservices-based architectures that fundamentally transform the security landscape through fragmented trust boundaries, multiple integration points, and complex failure modes. This article examines critical security challenges inherent in microservices architectures, including identity and authorization management across distributed systems, payment processing and transaction state management, data protection through tokenization, and internal trust boundaries requiring zero-trust principles. The article synthesizes findings from systematic literature reviews, empirical studies of open-source microservices systems, and practitioner surveys to demonstrate that security incidents in distributed commerce environments emerge primarily through subtle vulnerabilities during system degradation, partial outages, and cascading failures rather than direct external breaches. The article reveals that traditional perimeter-based security models prove insufficient for distributed microservices where services may number in the hundreds or thousands, requiring continuous identity validation, sophisticated token propagation mechanisms, and explicit state machines for transaction workflows. The article emphasizes that security and resilience must be treated as inseparable architectural concerns, with comprehensive strategies accounting for both the distributed nature of microservices and various failure modes that can compromise security guarantees. The article demonstrates that organizations must implement defense-in-depth approaches combining role-based access control, contextual authentication awareness, and zero-trust principles to maintain security integrity across independently deployed services maintained by different development teams with varying security expertise.
Supply Chain Integrity in the Generative AI Era: Evidence-Driven Controls from Source to Deployment
Software supply chain integrity has become a critical security concern as cloud-native delivery architectures expand dependency reuse and automation across build and deployment pipelines. Generative artificial intelligence accelerates software production but introduces risks through insecure code patterns while expanding artifact governance requirements to include model weights, prompt templates, and retrieval indexes. This article proposes a four-stage integrity framework spanning source, build, registry, and deployment that integrates secure development practices, artifact integrity controls, deployment-time policy enforcement, and operational response mechanisms. The principal contribution involves treating artificial intelligence artifacts as first-class supply chain objects requiring the same version control, integrity verification, and deployment governance as traditional software components. This addresses a significant gap in conventional container security practices that has become increasingly important due to the expanded attack surface of generative artificial intelligence systems. The framework enables organizations to produce auditable evidence of trust from source code and model artifacts through to running services, supporting both security objectives and compliance requirements in regulated environments.
Scalable Architecture for Large-Scale Salesforce Knowledge Management
Introduction: Enterprise information systems supporting customer relationship management functions face unprecedented challenges in managing the exponential growth of organizational knowledge assets while maintaining accessibility, accuracy, and regulatory compliance. Organizations generate vast quantities of support documentation, technical specifications, procedural guides, and troubleshooting resources requiring systematic organization and governance frameworks. Knowledge exists in multiple forms and locations throughout enterprises, making systematic management increasingly difficult as organizational complexity grows. Objectives: This article examines architectural patterns and governance frameworks for large-scale Salesforce Knowledge implementations within enterprise information systems contexts. The work addresses planning, design, and implementation challenges across technological, organizational, and social domains encompassing content lifecycle engineering, multi-dimensional governance, multimedia systems integration, and multi-platform service delivery. Methods: The architectural approach integrates workflow automation through Salesforce Flow, role-based access control mechanisms, and cloud computing infrastructure to achieve scalable knowledge delivery. Implementation frameworks address content lifecycle modeling from authoring through archival, organizational governance spanning business units and geographic regions, information security architectures incorporating authentication and authorization mechanisms, and multimedia systems supporting diverse content formats across heterogeneous consumption channels. Results: Empirical evidence from enterprise deployments demonstrates measurable improvements in operational efficiency with agent productivity gains of 40 to 45 percent and customer self-service adoption increases of 55 to 60 percent. First contact resolution rates improve by 25 to 30 percent while training duration reduces by 35 to 40 percent. Enterprise-scale validation through production deployments supporting 50,000 to 75,000 concurrent users and content repositories containing five million to eight million articles confirms architectural scalability. System availability exceeds 99.95 percent while comprehensive audit trails enable regulatory examination efficiency improvements of 40 to 50 percent. Conclusions: The governance model ensures regulatory compliance across General Data Protection Regulation and California Consumer Privacy Act requirements while maintaining content accuracy through automated lifecycle controls. Implementation outcomes validate socio-economic benefits including reduced service costs, enhanced customer satisfaction with Net Promoter Score improvements of 12 to 15 points, and accelerated organizational learning. Strategic implications emphasize holistic architectural approaches balancing technical capabilities with organizational readiness through executive sponsorship and phased implementation strategies.
Modernizing Legacy Enterprise Configuration Interfaces: A Bootstrap-Based Approach to Oracle Configurator UI Enhancement
User experience evaluation is critical for identifying user requirements and mitigating the risk of product failure during the development of interactive systems. This article offers a thorough framework for updating Oracle Configurator user interfaces using a Bootstrap-based method that keeps the backend infrastructure intact while providing modern frontend experiences. The persistence of legacy web infrastructures and database applications poses major obstacles to organizational agility, security, and maintainability. Outdated enterprise configuration systems lack modern security patches, responsive design, and integration capabilities, leading to operational inefficiencies and elevated risk. This article presents a four-step plan for updating systems that includes keeping the basic structure, redesigning the user interface, and adding interactive features based on the framework. The framework addresses three key challenges prevalent in enterprise software environments: diversified user goals and operational patterns, limited access to external users during development, and the necessity for long-term evaluation and maintenance. By carefully examining current Oracle Configurator designs and modern web development methods, this study shows how companies can greatly speed up configuration tasks, increase user involvement, and boost overall system performance. The dual-panel layout design featuring selection interfaces and real-time summary displays represents a significant advancement over traditional single-page configurator layouts. Post-implementation results from similar modernization initiatives demonstrate reductions in administrative overhead, improvements in security posture, and enhanced form load performance. The successful implementation validates the framework as a robust, scalable blueprint for organizations seeking to enhance their digital capabilities while preserving business logic and data integrity embedded in legacy Oracle systems.
Designing a Governed Lakehouse for Distributed Cloud Data and AI Systems: Practical Reference Architecture, Rollout Plan, and Trade-offs
Enterprise lakehouse initiatives can stall when governance is treated as tool configuration and informal process rather than as a first-class platform capability. Modern lakehouse deployments introduce multiple enforcement surfaces (batch engines, interactive SQL, gateways, services) and typically require high availability within a region (for example, across availability zones) [1]. In some organizations, deployments also span regions for disaster recovery (DR) or cross-region access—creating governance-state drift, inconsistent policy interpretation, and expensive manual audit work if control-plane semantics are not explicit. This paper presents a governance-first reference architecture for distributed lakehouse environments, emphasizing separation of control-plane responsibilities from data-plane execution. It provides a practical blueprint for (i) authoritative dataset identity and lifecycle management, (ii) policy specification, versioning, and portability across engines, (iii) schema evolution and contract-based change management, (iv) audit-grade evidence capture and provenance, and (v) governance-state distribution semantics for multi-instance, multi-AZ, and DR scenarios. The article also includes operational acceptance checks and an adoption roadmap to guide phased implementation and prevent governance debt as platform adoption and AI usage expand [8].
Empirical Findings: How Transformational Leadership Influences Employee Performance Through Intrinsic Motivation
This study investigates the relationship between transformational leadership and employee performance, emphasizing the mediating role of intrinsic motivation. A quantitative research design was employed, using structured online questionnaires distributed to 180 employees in the banking sector. Transformational leadership was measured via the Multifactor Leadership Questionnaire (MLQ), intrinsic motivation with the Intrinsic Motivation Inventory (IMI), and employee performance using the Work Tasks Performance Inventory. Data analysis included descriptive statistics, correlation, and hierarchical regression conducted in PSPP software. Findings indicate that transformational leadership positively influences employee performance and that intrinsic motivation serves as a significant mediator in this relationship. The results underscore the importance of developing transformational leadership competencies and fostering intrinsically motivating work environments. Recommendations are provided for managers to optimize employee performance, and directions for future research are suggested. The study contributes to theory and practice by clarifying the mechanisms through which transformational leadership affects employee outcomes.
The Relationship Between Transformational Leadership and Employee Performance: The Mediating Role of Intrinsic Motivation – A Methodological Approach
This study investigates the relationship between transformational leadership and employee performance, with a specific focus on the mediating role of intrinsic motivation. A quantitative research design was employed to collect primary data from employees in the banking sector through online questionnaires. The Multifactor Leadership Questionnaire (MLQ), the Intrinsic Motivation Inventory (IMI), and the Job Performance Scale (JPS) were used to measure transformational leadership, intrinsic motivation, and employee performance, respectively. A convenience sampling technique was adopted, with 180 participants selected based on accessibility and willingness to participate. The collected data were analyzed using descriptive statistics, correlation analysis, and hierarchical regression analysis to test the hypothesized relationships. Ethical considerations, including informed consent, confidentiality, voluntary participation, and adherence to The University of the West Indies’ (UWI) ethical protocols, were strictly observed. The methodological approach ensures reliable and valid data collection, providing a structured framework to examine the psychological mechanisms linking transformational leadership behaviors and employee outcomes. This research contributes to understanding how leadership practices foster motivation and performance in organizational settings.
Artificial Intelligence and the Skill Gap in Big Tech: Workforce Adaptation, Organisational Risks, and Emerging Competencies
Artificial intelligence has rapidly transformed organisational operations across the global technology sector, particularly within large technology corporations commonly referred to as Big Tech. While the integration of artificial intelligence systems has generated new efficiencies and innovation opportunities, it has simultaneously exposed a widening skill gap between organisational technological demands and the competencies available within the workforce. This study examines how the implementation of artificial intelligence in Big Tech firms influences workforce skill requirements, organisational risks, and professional development strategies. Using a qualitative research design, primary data were collected through structured open ended interviews with nine participants connected to the technology industry across multiple countries. The data were analysed using thematic analysis with the support of QDA Miner software, allowing the identification of recurring patterns within participant responses. The findings reveal five major themes. First, the perceived value of formal education has shifted as employers increasingly prioritise artificial intelligence related competencies over traditional academic credentials. Second, organisations now demand a combination of advanced technical capabilities and adaptable soft skills, particularly in areas such as artificial intelligence integration, cloud computing, and data analysis. Third, the skill gap introduces significant organisational risks, including shortages of specialised talent, ethical concerns surrounding artificial intelligence usage, and the potential overestimation of technological capabilities. Fourth, Big Tech firms are responding through large scale upskilling and reskilling initiatives aimed at strengthening workforce readiness. Finally, participants widely perceive artificial intelligence as a complement rather than a replacement for highly skilled human labour, although routine tasks may continue to be automated. The study contributes to current discourse on technological labour market transformation by providing qualitative insight into how individuals within the technology sector interpret and respond to the evolving demands created by artificial intelligence adoption.
A Literature Review of Artificial intelligence, skill gap, Big Tech, upskilling, reskilling, workforce development, labour market transformation, human AI collaboration
This literature review examines the impact of artificial intelligence (AI) on workforce skills and organisational dynamics within “Big Tech” firms, with a particular focus on the emerging skill gap, workforce development, and the interplay between human labour and AI. The review synthesizes both qualitative and quantitative sources, including academic journals, industry reports, and corporate initiatives from leading technology companies such as Apple, Microsoft, Amazon, Google, and Meta. Key themes explored include the diminishing value of traditional educational credentials, the shift in demand toward AI-related and technical skillsets, and the strategic responses of organisations through upskilling and reskilling programs. The review further investigates the complementary and substitutive roles of AI in the workforce, highlighting a hybrid outcome where automation coexists with enhanced human productivity. Finally, gaps in the existing literature are identified, including the need for longitudinal research, integrated frameworks linking AI adoption to workforce outcomes, and evaluations of long-term effectiveness of organisational skill development strategies. The findings provide a comprehensive understanding of how AI-driven technological transformation is reshaping skills, labour market expectations, and human AI collaboration within the technology sector.