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.
Copyright (c) 2026 Journal of Information Systems Engineering and Management
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.
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.
The Relationship between Transformational Leadership and Employee Performance and the Mediating Role of Intrinsic Motivation: A Literature Review
Leadership remains a central determinant of employee motivation and performance in contemporary organizations. Among the various leadership approaches, transformational leadership has gained considerable attention for its ability to inspire employees, foster commitment, and enhance organizational outcomes. This article presents a comprehensive literature review examining the relationship between transformational leadership and employee performance, with a particular focus on the mediating role of intrinsic motivation. Drawing upon the principles of Albert Bandura’s Social Cognitive Theory, the study synthesizes theoretical and empirical research to explain how leadership behaviors influence employee motivation and performance outcomes. Existing literature indicates that transformational leadership positively influences employee performance by encouraging intellectual stimulation, individualized consideration, inspirational motivation, and idealized influence. Furthermore, intrinsic motivation emerges as a key psychological mechanism through which transformational leaders influences employee attitudes and behaviors. Employees who experience higher levels of intrinsic motivation tend to demonstrate greater engagement, persistence, and commitment, which ultimately improve performance outcomes. The review highlights that intrinsic motivation is influenced by the fulfilment of psychological needs such as autonomy, competence, and relatedness, as described in Self Determination Theory. By examining the relationships among transformational leadership, intrinsic motivation, and employee performance, this article proposes a conceptual framework and research hypotheses that contribute to a deeper understanding of leadership effectiveness in organizational settings. The findings provide insights for scholars and practitioners seeking to enhance employee performance through motivational and leadership strategies.
The Effect of Rotation on The Boundary Layer Separation
In this study, flow control on a NACA 0012 airfoil is investigated under various rotational speeds at a Reynolds number of Re = 3.6 × 10⁵. The commercial computational fluid dynamics (CFD) software, ANSYS Fluent, is utilized to perform the simulations. Both rotating and non-rotating cases are examined at a fixed angle of attack of 18° to assess the effects of rotation on aerodynamic performance To analyze the stall-delay phenomenon in rotating airfoils, a combination of boundary layer theory, numerical simulations, and experimental measurements is employed. The results indicate that rotation significantly influences the flow behavior by delaying the onset of flow separation compared to the stationary (non-rotating) two-dimensional case. This rotational effect contributes to improved aerodynamic stability and lift characteristics, highlighting the potential of rotational motion as a passive flow control method for enhancing airfoil performance in relevant engineering applications.
Proactive MEC Resource Allocation Using Deep Learning for Real-Time Collision Avoidance in Connected Vehicular Networks
This paper proposes a proactive resource allocation framework for multi-access edge computing (MEC) systems in connected vehicular networks, targeting real-time collision avoidance applications. In such environments, dynamic workload variations caused by vehicle mobility and traffic density can lead to inefficient resource utilization and latency violations under conventional reactive allocation strategies. To address this challenge, we develop a deep learning-based prediction model using a Gated Recurrent Unit (GRU) to forecast short-term CPU and memory demand. The predicted workload is integrated into a latency-aware orchestration mechanism that performs proactive resource allocation under capacity constraints. Extensive simulations using real-world vehicular mobility traces demonstrate that the proposed approach significantly improves system performance. In particular, it reduces latency and SLA violations by more than 50% compared to reactive allocation strategies, while improving CPU utilization efficiency. The results highlight the effectiveness of combining deep learning-based workload prediction with proactive resource orchestration for next-generation MEC-enabled vehicular systems.
Unveiling AI Adoption in SMEs: A Systematic Review of Perceived Advantages and Barriers Through an Extended TAM Framework
Introduction: This systematic literature review examines the advantages and disadvantages SMEs see in adopting AI. Objectives: The objectives of the study were to categorize the benefits (perceived usefulness and performance expectancy) and barriers (perceived ease of use, effort expectancy, facilitating conditions, and price value) of AI adoption among SMEs, and to develop a theoretical framework that illustrates their relationships. Methods: The study used a TAM that was extended by adding components from TAM2, UTAUT, and UTAUT2. In accordance with the PRISMA guidelines, our analysis included 87 studies identified in the Scopus database from 2015 through 2024. Results: The results indicated 14 primary benefits of adopting AI and 7 primary barriers, most of which were internal or external. Co-occurrence analysis identified several ways to address barriers to AI adoption, including reducing the cost of AI solutions to alleviate financial burdens on SMEs, improving operational performance, and addressing funding issues that affect SMEs. A conceptual model has been developed that links the various dimensions to the intention to adopt AI, with moderators that affect the likelihood of an SME adopting AI. In addition, the findings demonstrate that SMEs’ flexibility enables them to adapt to the rapid changes in digital transformation. Conclusions: The findings indicate that researchers should design studies to evaluate the long-term effects of adopting AI. Overall, this study brings together fragmented prior research, thereby enabling SMEs to leverage AI to enhance innovation, operational effectiveness, and competitive advantage during times of economic uncertainty.
Explainable Multimodal Deep Learning Framework to predict cardiovascular disease with heterogeneous clinical data
Cardiovascular disease (CVD) stands as the top cause of death in the global world, with it being a large burden to healthcare systems. Classical risk assessment models like Framingham and SCORE tend to have several constraints: they consider only a few clinical variables, and they are not sufficient to represent the complexity and heterogeneity of modern healthcare data, such as electronic health records (EHR), laboratory values, electrocardiogram (ECG), medical images, and lifestyle determinants. The proposed work hypothesizes a explainable and multimodal deep learning system of realizing accurate and interpretable prediction of CVD through an integration of the various clinical data sources. The model to be proposed uses modality-specialized encoders to derive high-level representations of both structured and unstructured data, then a fusion mechanism is used that gets the interactions across the modalities. The framework is taught and assessed on a multi-institutional data set of more than 10,000 patients, which consists of EHR, laboratory outcomes, ECG, and imaging characteristics. To improve the degree of transparency, explainability methods (SHAP (Shapley Additive Explanations)) and Grad-CAM are also integrated to discover clinically significant attributes to use in predictions. The experimental performance of the proposed approach proves better than the performance of the traditional machine learning, and unimodal deep learning models with AUC, sensitivity, and specificity equal to 0.93, 0.90, and 0.88, respectively. The results say that the framework does not only enhance the predictive accuracy but also offers valuable information regarding the model decisions and it is hence appropriate in application in clinical decision support.
AgroVision AI The Proactive Crop Health Monitoring and Early Disease Detection: A Smart Computer Vision Framework
The high global agricultural output has been threatened by crop diseases, pests and weed infestations which result in high loss in yield as well as high operating expenses. Conventional solutions of monitoring are mainly based on manual inspection that is very time consuming and labor consuming as well as is not effective when it comes to detecting at an initial stage. This paper suggests a solution to the mentioned problems, the AgroVision, an intelligent computer vision-based framework that is capable of monitoring crops health in real-time and detecting plant diseases at the earliest stage. The suggested system will make use of the advanced machine learning and deep learning algorithms, specifically Convolutional Neural Networks (CNNs), that will be used to analyze image-based plants automatically and identify the visual symptoms of diseases, pests and weeds. An effective dataset of various types of crop images in different environmental factors is presented to be trained and analyzed. The model is optimized with accuracy and efficiency performance which is high in comparison and the model has better generalization performance in field conditions. The findings of the experimental work indicate that the AgroVision can effectively identify various crop pathogens during early stages hence interventional response can be followed and the losses minimized. Moreover, the system promotes real-time deployment hence is appropriate in the use of precision agriculture. Its major contributions consist of a high-quality and scalable framework on the basis of vision, higher detection rates with the help of the deep learning model, as well as the inclusion of automated monitoring that will reduce human input and maximize the sustainability of agriculture.
An Automated Linux Security Auditing and Hardening Framework
Linux systems are increasingly targeted by cyber-attacks due to misconfigurations, outdated packages, and exposed services. Traditional security assessment approaches are often manual, time-consuming, and fragmented across multiple tools. This paper presents a unified automated framework for Linux security auditing and hardening, integrating vulnerability assessment, CVE-based mapping using the National Vulnerability Database, automated remediation, patch management, network port scanning, and real-time security monitoring. The framework is implemented using a hybrid architecture combining Python, Bash scripting, and a Flask-based monitoring interface. It was evaluated across six widely used Linux distributions Ubuntu, Debian, CentOS, Fedora, Kali Linux, and Parrot OS using a systematic before-and-after hardening methodology. Experimental results demonstrate a 60 – 70% average reduction in detected vulnerabilities and misconfigurations, improved security scores, and minimal runtime overhead. Additionally, the framework introduces a closed-loop automated security pipeline that enables continuous assessment and remediation without manual intervention.These findings validate the framework’s effectiveness, scalability, and portability, providing a practical solution for proactive Linux system security management. Future work will extend the framework to container and application-level security and incorporate machine learning-based anomaly detection.
From Centralized Data Lakes to Data Mesh: Lessons from Large-Scale Enterprise Transformations
Centralized data lake architectures have long served as foundational infrastructure supporting enterprise analytics capabilities across diverse organizational contexts. Contemporary organizations, however, face increasingly significant challenges inherent in monolithic data models, including unclear ownership boundaries, diminished governance agility, and progressively degraded data quality as organizational scale expands beyond the capacity of centralized management paradigms. Domain-oriented data mesh architectures offer viable alternatives to these rigid centralized frameworks by fundamentally shifting architectural philosophy from technology-centric consolidation strategies toward domain-driven distribution models that align data ownership with organizational expertise boundaries. This article comprehensively addresses the structural deficiencies inherent in consolidated repository architectures while articulating the paradigm shift that distinguishes data mesh implementations from incremental improvements to existing centralized models. The data-as-a-product conceptualization receives detailed analytical attention alongside federated governance mechanisms that enable distributed policy enforcement without creating centralized bottlenecks. Self-serve infrastructure platform requirements form critical components of successful transformations, providing domain teams with standardized capabilities while maintaining autonomy in data product development and operation. Metadata-driven policy enforcement combined with standardized interoperability contracts enables organizational scalability without compromising stringent compliance standards across regulatory domains. Migration strategies supporting incremental transformation while preserving operational stability throughout transition periods constitute essential considerations for enterprise adoption, as organizations cannot afford disruption to critical analytical workloads during architectural evolution. The architectural philosophy underlying data mesh implementations strategically rebalances centralized control mechanisms with domain autonomy through explicit separation of platform capabilities from domain-specific responsibilities. A comprehensive enterprise transformation case study demonstrates practical implementation patterns across a multi-year migration, providing empirical evidence of achievable outcomes. Architectural diagrams visualize structural distinctions between paradigms, while a detailed maturity assessment framework enables organizations to evaluate transformation preparedness systematically. Data mesh represents an evolutionary advancement in enterprise data management rather than a complete infrastructure replacement requiring abandonment of existing investments. Enterprise-scale data platforms increasingly benefit from distributed ownership models aligned with business capability boundaries, enabling horizontal scaling that grows naturally with organizational expansion. Federated computational governance ensures consistency across organizational domains without creating the centralized bottlenecks that characterize traditional governance approaches. The synthesis illustrates pathways through which organizations can achieve sustainable data architecture capable of accommodating growing operational complexity while maintaining quality standards and regulatory compliance.
Communicating Innovation in Distributed Semiconductor Design Platforms
The increasing complexity of semiconductor systems and the global distribution of design teams have intensified the need for effective communication within distributed semiconductor design platforms. This study investigates how communication practices influence innovation outcomes in such environments by conceptualizing communication as a multidimensional construct encompassing clarity, frequency, integration, and transparency. Using a mixed-methods research design, the study combines survey-based quantitative analysis with qualitative insights from practitioners involved in distributed semiconductor design projects. Empirical results demonstrate that communication maturity has a significant positive effect on innovation performance, with communication integration emerging as the strongest predictor of design efficiency, quality improvement, rework reduction, and innovation throughput. Visual and statistical analyses further reveal that structurally embedded and transparent communication mechanisms outperform ad-hoc interaction models in sustaining innovation velocity. The findings highlight communication as a strategic capability rather than a support function and offer practical guidance for designing communication-aware distributed platforms that enhance innovation in semiconductor ecosystems.
Quality Engineering for Reliability in Distributed Cloud, Edge, and IoT Platforms
Modern enterprise platforms operate as distributed ecosystems spanning cloud-native services, edge computing infrastructure, and interconnected Internet of Things (IoT) environments. Traditional quality assurance models, designed for centralized architectures and sequential development lifecycles, are insufficient in environments characterized by asynchronous communication, heterogeneous execution layers, and physical-world dependencies. This article argues that quality engineering must be elevated to an architectural discipline capable of governing reliability across distributed systems. Through applied patterns drawn from large-scale enterprise deployments, it demonstrates how structured validation frameworks—embedded within system design and extended by observability-driven feedback loops—produce measurable improvements in synchronization integrity, transaction reliability, and cross-platform data consistency. Deployments adopting architecture-driven validation have achieved cross-platform data consistency rates exceeding 99% while reducing reconciliation discrepancies across distributed services. These outcomes support the position that quality engineering, when treated as foundational infrastructure rather than a downstream function, is the primary mechanism for sustaining reliability across cloud, edge, and IoT platforms.
Micro-Clouds: A Resilient Edge Framework for Regional Educational Infrastructure
Modern educational infrastructure encompassing student information systems, library catalogs, and learning management platforms has migrated rapidly to centralized public cloud architectures. While this shift delivers significant operational convenience, it has imposed a systemic "Connectivity Tax" on regional and underfunded school districts. These districts depend on fragile Wide Area Network backhauls that are vulnerable to extreme weather events, physical line breaks, and global cloud provider outages. The Micro-Cloud framework proposes a localized, high-resiliency compute and AI deployment model situated within county boundaries. Built upon lightweight container orchestration and a Hybrid-Edge model that synchronizes with the public cloud during normal operations, the system provides autonomous "Lifeboat" mode functionality during total network isolation. By incorporating localised Small Language Models, the framework ensures essential digital services and AI-assisted tutoring remain available even during prolonged connectivity loss. A simulated 48-hour isolation test demonstrates 98.2% service availability against 0% for a public-cloud-only control group, while a modest hardware investment of approximately USD 3,350 eliminates fate-sharing risks inherent to hyperscale cloud providers.
AI-Powered Clinical Document Intelligence: Automating Data Extraction, Standardization, and Review
The pharmaceutical and life sciences industry continues to struggle with extracting, standardizing, and ensuring regulatory compliance for unstructured clinical documents. While natural language processing and machine learning offer theoretical solutions, existing literature treats clinical document intelligence (CDI) as isolated technical problems rather than integrated regulatory systems. This article synthesizes current knowledge on transformer-based NLP, optical character recognition, and automated coding within the constraints of FDA 21 CFR Part 11 and AI/ML validation frameworks. The article critically examines seven domain areas—NLP architectures, document processing, coding automation, workflow integration, cloud infrastructure, regulatory compliance, and responsible AI, while identifying persistent gaps: validation methodologies suited to stochastic systems, evidence-based bias mitigation procedures, and deployment considerations that bridge technical capability and regulatory reality. By positioning clinical document intelligence as a regulatory systems integration challenge rather than a pure machine learning problem, this article provides a framework for practitioners and researchers to evaluate whether existing techniques adequately address pharmaceutical operational requirements. The article does not claim to advance the underlying technologies but rather articulates how known techniques intersect with regulated environments, where human oversight, explainability, and equity must be engineered into systems from the start.
Robust Nonlinear Control Based on Artificial Intelligence for Electric Vehicles Under Several Constraints
In this paper, we propose a neural network algorithm to eliminate uncertainties caused by dynamic compensators used to linearize permanent magnet synchronous motors for electric vehicle traction. To do this, we propose a speed controller based on an artificial neural network algorithm to approximate the dynamics and correlate the uncertain environmental parameters. We first estimate these uncertainties and use them as inputs to the ANN. We then find an adaptation law to eliminate the uncertainties caused by the compensators. The effectiveness and success of the proposed approach compared to classical controllers in different scenarios of electric vehicle traction are demonstrated by simulations of the adaptive ANN on Matlab/Simulink.
An Explainable Deep Learning Approach for Diabetic Retinopathy Classification and Precise Lesion Segmentation
Diabetic Retinopathy (DR) is a leading cause of vision impairment and blindness among diabetic patients worldwide, necessitating early and accurate diagnosis for effective treatment. This study proposes an explainable deep learning approach for diabetic retinopathy classification and precise lesion segmentation using a hybrid architecture that integrates Convolutional Neural Networks (CNNs) and Transformer-based attention mechanisms. The proposed framework is designed to automatically identify the severity level of DR from retinal fundus images and accurately localize pathological regions such as microaneurysms, hemorrhages, and exudates through advanced segmentation techniques.The CNN component extracts hierarchical spatial features from input retinal images, while the Transformer module captures long-range contextual dependencies to enhance global feature representation. To improve clinical trust and interpretability, Explainable Artificial Intelligence (XAI) techniques such as Grad-CAM and attention map visualization are incorporated, enabling clinicians to understand model decision-making and validate highlighted lesion regions.The segmentation module employs a hybrid encoder–decoder architecture with multi-scale feature fusion to precisely delineate infected areas, improving diagnostic transparency and reliability. Experimental evaluation on benchmark retinal image datasets demonstrates superior performance in terms of classification accuracy, sensitivity, specificity, Dice coefficient, and Intersection-over-Union (IoU) compared to conventional deep learning models.The proposed explainable framework not only enhances diagnostic accuracy but also strengthens clinical interpretability, making it a reliable decision-support tool for automated diabetic retinopathy screening and early intervention in real-world healthcare settings
Conflict and Coexistence between Urban Agriculture and Urban Expansion: The Case of N'Gaous City, Northeast Algeria (1966–2024)
Urban Agriculture (UA) has recently attracted growing attention from researchers and policymakers as a strategic component of sustainable urban planning and food systems [1, 2]. This research explores the complex dynamics of coexistence and conflict between urban expansion and agricultural practices in the city of N'Gaous (northeast Algeria)—a small city characterized by urban agricultural pockets deeply interwoven with the urban fabric, which has experienced significant spatial growth and demographic pressure between 1966 and 2024 [3, 4]. Using a mixed methodology combining quantitative spatial analysis (Land Use/Land Cover classification—LULC, and AHP-GIS multi-criteria decision-making model) with qualitative field verification (interviews and participatory mapping), the study aimed to identify and assess areas prone to conflict or coexistence between these two land uses. Findings reveal that N'Gaous lost approximately 239.4 hectares of agricultural land (37%) between 1966 and 2024, driven primarily by demographic growth and urban expansion. The AHP-GIS model (weights: building density 25%, population density 20%) identified three main zones: high-conflict zones (23%), moderate coexistence zones (41%), and high-compatibility zones (36%). Field verification confirmed the model's accuracy. The study also reveals that the continuity of urban agriculture in N'Gaous is linked to the resilience of adaptive farming practices and a shift towards high-value crops. These findings offer a robust methodological framework for decision-makers in small and medium-sized Algerian cities facing similar challenges, emphasizing the importance of recognizing urban agriculture as an essential component of urban resilience and food security [8, 9]. The study contributes to current literature on peri-urban transitions in North Africa and offers methodologically applicable insights for similar contexts.
AI Security Regulatory Gaps: A Global Comparative Review and a Governance Blueprint Tailored for the United Arab Emirates
Artificial intelligence (AI) is being adopted at unprecedented speed across public and private sectors, while regulatory responses diverge widely across jurisdictions. The European Union (EU) AI Act inaugurates a harmonized, risk-based regime; the United States and United Kingdom pursue sectoral or principles led approaches; Singapore and China implement prescriptive models; and Gulf Cooperation Council (GCC) states, including the United Arab Emirates (UAE)have favored nonbinding strategies and sandboxes. Building on a scoping of 48 publications, this review synthesizes advances in AI governance, highlights unresolved security and enforcement gaps, and proposes a UAE tailored framework aligned with ISO/ISMS and international best practices. We find persistent weaknesses in risk classification fidelity, security-by-design obligations, policy–practice enforcement, and governance for general purpose and agentic AI. We propose (i) a global AI security and governance framework and (ii) a UAE governance blueprint with tiered obligations for critical sectors, conformity assessment, incident reporting, and independent audits. The review contributes a practical compliance checklist and legislative recommendations forming the basis for a UAE AI Act, alongside an implementation roadmap.
Impact of Social Media Marketing on Buying Decisions and Career Choices of Generation Z in the Cosmetic Industry
Introduction: Generation Z is rapidly emerging as an influential segment of today’s global workforce. Across industries, including the cosmetic sector, this generation brings new perspectives, innovation, strong digital capabilities and digital awareness. The cosmetic industry, which is highly driven by trends, innovation and consumer engagement, increasingly seeks young talent who can understand the dynamic digital marketplace and evolving customer preferences. Objectives: This study explores the impact of social media marketing on the buying decisions and career perceptions of Generation Z in the cosmetic industry and highlights how Gen Z employees contribute to innovation, trend awareness and better understanding of younger consumer markets. It also examines digital marketing strategies such as influencer promotions and online brand campaigns used by cosmetic companies. Methods: The study is based on a review of existing academic literature related to Generation Z consumer behaviour, social media marketing, and digital engagement in the cosmetic industry. Results: The findings indicate that Generation Z contributes to innovation, trend awareness and better understanding of younger consumer markets. Social media platforms and influencer marketing significantly influence their purchasing decisions and career interests. Conclusions: While Generation Z offers advantages such as creativity and digital proficiency, organizations must address the growing role of digital platforms in shaping both consumer behaviour and career perceptions.
Federated Learning for Privacy-Preserving Chronic Pain Rehabilitation Outcome Prediction Across Multi-Centre Physiotherapy Clinics
Rehabilitation of chronic pain in various physiotherapy centres generates a significant amount of detailed long-term patient information that can be used to forecast. But laws on data privacy, such as GDPR Article 9 and HIPAA, prevent us from collecting this data in a single location, which is necessary in conventional machine learning processes. This paper presents a Federated Learning architecture that enables the prediction of the results of rehabilitation in five physiotherapy clinics, and patient data remains confidential. It addresses two key problems: providing privacy and addressing disparities in data distribution among clinics that cannot be addressed by standard federated approaches. To prevent inter-clinic drift in the system, a combination of FedProx and proximal regularisation ( μ =0.01) are used. It additionally employs three privacy protection layers: noise addition to data (differential privacy, ε = 3.2, δ = 10 -5) with the help of Rényi accounting, safe cryptographic aggregation, and encrypted communication with the help of TLS. Such procedures comply with GDPR Article 89 and HIPAA conditions. The system consists of a local model with two components that process both static and time-based data with a Dense network and an LSTM network, respectively, enabling the system to learn about initial health risks and the time-based variation of recovery. It was tested on 5,000 synthetic patient records with 43 validated variables in five clinics in five-fold cross-validation and leave-one-clinic-out testing. It achieved a weighted F1 score of 0.774, AUC-ROC of 0.861, and Cohen's κ of 0.661. This is 7.1 percentage points better than local training and only 4.7 percentage points worse than a centralised model that does not safeguard privacy. The privacy measures only reduced the F1 score by 1.5 percentage points. The four-class prediction target is clinically meaningful, as indicated by clinical results of 30.2% reduction in pain (VAS), 27.5% decrease in disability (ODI), and a 14.3% improvement in physical health scores (SF-36). These findings demonstrate that privacy-preserving federated learning can deliver useful predictions of rehabilitation outcomes in various clinics without causing damage to patient privacy or violating the law.
A Parallel Intelligence Framework Integrating Digital Twins for Smart City Cyber-Physical-Social Systems: Architecture, Methodology, and Case Studies
Introduction: The accelerating urbanization of the twenty-first century has transformed cities into hyper-connected cyber-physical-social systems (CPSS) whose complexity exceeds the descriptive capacity of classical modeling paradigms. Two complementary research traditions—parallel intelligence (PI) grounded in the ACP approach and digital twins (DT) based on high-fidelity virtual replication—have emerged in response but have evolved largely in isolation, with PI emphasizing social behavior and policy learning while DT emphasizes physical fidelity and real-time synchronization. Objectives: This paper proposes PI-DT-CPSS, a unified framework that integrates parallel intelligence and digital twins to govern complex urban systems, organizing a smart city into five interoperating layers—physical, digital twin, parallel intelligence, social, and decision support—and formalizing their interaction through a closed feedback loop. Methods: We detail the mathematical formulation of multi-fidelity twin models, a data fusion procedure combining Kalman filtering with deep residual correction, and a parallel execution strategy that combines model-predictive control with deep reinforcement learning. Results: The framework is validated through three case studies: adaptive traffic signal control, renewable-dominant microgrid management, and urban emergency evacuation. Across these domains, simulation experiments show reductions of 19–34% in key performance indicators relative to state-of-the-art baselines, while preserving human-in-the-loop accountability. Conclusions: We conclude by discussing theoretical contributions, deployment implications, and open challenges in privacy, fairness, and computational scalability
AI-Driven and Explainable Detection of Fraud and Cyber-Related Risks in Critical Infrastructure Payment Networks
Digital payment networks which face significant security breaches from financial fraud and cyber attacks have become essential for critical infrastructure sectors that encompass energy and healthcare and transportation and government systems. The detection of complex and evolving attack patterns which use coordinated methods fails to be achieved by traditional rule-based systems when they operate in environments with highly imbalanced transaction volumes. The research presents an explainable artificial intelligence framework which detects fraudulent and anomalous activities in payment systems that serve critical infrastructure. The framework uses real-world transactional data from the IEEE-CIS Fraud Detection Dataset to combine supervised and unsupervised learning techniques which include Logistic Regression and Random Forest and XGBoost for fraud classification and anomaly detection for irregular transaction behavior. To handle class imbalance the Synthetic Minority Oversampling Technique (SMOTE) is used whereas threshold optimization enhances the system's ability to identify different types of detection with better precision. The framework uses SHAP to provide explainable AI which produces interpretable results that show how models make predictions and which features affect their predictions. The proposed approach successfully identifies high-risk transactions because it improves key evaluation metrics which include precision and recall and F1-score and AUC. The proposed framework provides a scalable and adaptable solution for securing payment systems that underpin critical infrastructure operations. This work creates financial fraud detection and cyber risk awareness systems which build resilient and transparent and intelligent security solutions that protect national economic stability and infrastructure systems
Explainable Artificial Intelligence (XAI): Promoting the transparency and trust in machine learning models.
The swift proliferation of machine learning and deep learning models into the domain of sensitive areas like healthcare, finance, and the law has created a great worry about their lack of transparency and interpretability. These models tend to run as black box, thus restricting their usage in high stakes situations as the users might not understand, trust or even validate their decisions [10], [13]. Explainable artificial intelligence (XAI) has become a promising paradigm to deal with these two issues by making models explain themselves in human understandable terms and not engaging in predictive performance to a considerable degree [33]. This paper provides an in-depth and organized summary of XAI along with its major concepts in relation to interpretability, transparency, trust, and accountability. It also divides XAI techniques into intrinsically interpretable (ante-hoc) models and post-hoc explanation methods and differentiates between model-specific and model-agnostic [13], [39]. The paper also looks at the association between various kinds of explanations and user confidence in various fields of applications. It suggests a new conceptual framework that demonstrates the connection between technical attributes of explanations, including fidelity, stability, and completeness, and human-related aspects, including trust, understanding, and fairness. The main contributions made by this work are: a taxonomy of XAI methods are complete aligned with model architectures and data modalities, a more integrated framework with explanation types, their target users, and measures of evaluation, provides deeper insight into the open issues of standardization of evaluation, faithfulness of their explanations and regulatory adherence [38]. In general, the proposed research should inform researchers and practitioners to choose, design, and assess XAI methods to improve the transparency, reliability, and trust in the current AI systems.
Drivers of Behavioral Intention Toward AI Integration in Accounting Education: A UTAUT2 Perspective from Bangladeshi Universities
The incorporation of Artificial Intelligence (AI) in accounting education is altering approaches to learning and professional skills development significantly at the higher level of study. Based on the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2), this study examines the determinants that drive accounting students’ behavioral intention towards AI adoption in Bangladeshi universities. Data was obtained from 692 accounting undergraduate, and MBA students enrolled in public and private universities using a structured questionnaire that contains 28 observed variables. Factor analysis (FA) with varimax rotation was used to explore the factor structure. The KMO estimate was 0.950, and the Bartlett’s Test of Sphericity demonstrated the appropriateness of conducting a factor analysis. The results indicate eight major factors – Performance Expectancy, Facilitating Conditions, Behavioral Intention, Social Influence, Price Value, Habit, Effort Expectancy, and Hedonic Motivation, with a total variance of 84.96%. The most powerful predictor was Performance Expectancy, which could account for 52.00% of the variation, followed by Facilitating Conditions and Behavioral Intention. The findings suggest that students’ use intentions regarding AI in accounting education are primarily influenced by perceived performance benefits, infrastructural support, career relevance, and cost–benefit rationales. The findings of the study provide good implications for policy makers, university authorities, and curriculum developers to identify the strategic way forward to working on AI integration effectively with accounting education in developing countries like Bangladesh.
Detecting Digital Investment Scams: The SDQC User-Level Screening Framework for Malaysian Gig Workers
Digital investment scams targeting gig workers and freelancers in Malaysia have intensified with the expansion of cryptocurrency markets and AI-driven platforms, yet individuals in precarious employment lack access to structured tools for evaluating fraudulent opportunities. This study conceptualises digital scam vulnerability as a user-level decision support problem and develops the Scam Detection Quick-Check (SDQC), a six-dimensional screening framework derived from the synthesis of behavioural, regulatory, and informational scam risk indicators. The study employs a conceptual design supported by targeted literature synthesis and illustrative case analysis of three high-profile Malaysian investment scams: Lavida Coin, Harapan Coin, and JJPTR. The SDQC framework comprises six screening dimensions, namely Source Verification, Credibility Assessment, Offering Clarity, Disclosure and Regulation, External Validation, and Timing Awareness, each mapped to a distinct class of scam vulnerability identified in the literature. Case analysis shows that all three Malaysian cases failed across multiple SDQC dimensions, illustrating the framework’s diagnostic relevance in real-world scam contexts. The study makes three scholarly contributions: it reframes individual scam susceptibility as a structured screening problem; it develops the SDQC from literature synthesis and case-based reasoning; and it extends scam prevention research into the digital gig economy context in Malaysia. The SDQC offers a practical user-level screening tool applicable to financial literacy programmes, platform-integrated warning systems, and regulatory guidance in Malaysia and comparable digital labour markets.
Analysis of Subordinate Clauses in Arabic by using Applicative Combinatory Categorial Grammar
The model of Applicative Combinatory Categorial Grammar (ACCG) [1] [2] [3] [4], which employs combinators and reduction mechanisms derived from combinatory logic, constitutes an effective applicative system for handling various types of propositions, whether simple or complex, in certain languages. ACCG is a system that transforms concatenated expressions in natural language into applicative expressions that provide a functional-semantic interpretation for verifying sentence structure. The objective of this study is to analyze subordinate clauses in Arabic using the ACCG framework.
IDENet-LF: Implicit Degradation Estimation for Blind Light-Field Super-Resolution
We propose IDENet-LF, a blind light-field spatial super-resolution (SR) framework that replaces the explicit degradation estimator used in prior LF blind SR pipelines with the lightweight implicit degradation estimation network (IDENet). By integrating IDENet as the first module and retaining the original light-field restoration (LF-SR) backbone from LF-DEST, the proposed system jointly benefits from IDENet’s efficient implicit degradation representation and LF-DEST’s spatial-angular restoration capabilities. The framework is tested on public light-field benchmarks under diverse degradations, shows improved robustness to unknown blur and noise kernels. Extensive experiments on multiple LF benchmarks demonstrate that IDENet-LF achieves comparable performance to LF-DEST, improving PSNR by up to 0.2 dB. The results confirm that implicit degradation modeling is a promising direction for efficient and robust blind LF super-resolution.
Multimodal AI Integration in Healthcare Information Systems: Unifying Imaging, EHR, and Wearable Data for Early Diagnosis
Multimodal artificial intelligence has found a significant direction in healthcare information systems since contemporary diagnosis is conditioned by the combination of several evidence streams as opposed to a single clinical signal. The medical imaging also offers anatomical and pathological data, electronic health records it offers longitudinal clinical history, and continuous physiological monitoring can be located out of the hospital with the help of wearable devices. The study project is a multimodal AI-driven healthcare information architecture that combines imaging, EHR, lab, clinical-note, and wearable-sensor data to diagnose early diseases. The suggested system works with the standardized data acquisition, interoperability protocols, preprocessing, multimodal fusion, and multimodal feature extraction, followed by clinical decision support, explainability, and ongoing monitoring to produce the early diagnostic risk score. The proposed architecture is assessed based on a simulated experimental setup with the realistic performance metrics such as accuracy, sensitivity, specificity, and F1-score as well as AUC, latency, missing-data robustness, and interpretability of the outcomes by clinicians. The findings indicate that multimodal fusion has the potential to enhance the performance in early diagnosis as opposed to single-modality systems since the sensor combines structural, temporal, behavioral, and clinical context. Significant implementation issues outlined in the study, among them being data heterogeneity, interoperability gaps, privacy protection, model bias, alert fatigue and prospective clinical validation. The conclusion of the paper is that multimodal AI must be used as a clinician-focused decision-support layer as opposed to an autonomous diagnostic solution.
Income Inequality and Economic Growth in China: Regional and Urban-Rural Disparities (1978–2020)
Income disparity continues to afflict China, especially after the policy reforms adopted in 1978, which aimed to improve economic growth. This growth benefited several high-income earners and more urbanized regions, especially coastal regions, which gained disproportionately greater benefits from industrialization and market liberalization. Since the late 2000s, different governmental approaches, such as redistributive policies and infrastructural investment in rural areas, have worked together to overcome inequalities. However, these efforts have offered only a partial remedy to the menace. This study draws on research insights and statistical data, to analyse the factors of inequality such as the rural-urban divergence and regional economic imbalances. Moreover using the Gini coefficient, we explore the relationship of growth and economic inequality. The paper highlights China’s efforts to alleviate poverty and overcome this inequality which has become increasingly prevalent after the 1978 reforms. The findings thereby stress the importance of taking measures to maintain a balance between growth and equity, and bring attention to the required policies aimed at more sustainable and inclusive growth.
Mastering Enterprise Data: A Comprehensive Guide to Centralized Master Data Governance
SAP Master Data Governance (SAP MDG) has emerged as the comprehensive, enterprise-grade solution enabling organizations to establish centralized governance frameworks that deliver end-to-end data strategy, validation, workflow, and replication of customer, supplier, material, and financial information across business domains through configurable architectures. As organizations globally struggle with data quality—with managers reporting unacceptable levels of faulty decision support data and high levels of errors in enterprise master data maintenance—SAP MDG stands out by delivering sophisticated capabilities through flexible data models for core entities like Business Partner (BP) and Material Master, advanced change request workflow management, and the SAP Business Rule Framework Plus (BRF+). SAP MDG provides the complete data lifecycle management needed to achieve regulatory scrutiny while enabling organizations to move beyond fragmented, legacy approaches to master data governance. The transformation of master data management and data stewardship through SAP MDG's interoperability between protocols and standards, AI-based automation through intelligent matching and cleansing, and cloud-based SAP MDG Cloud Edition deployment options positions organizations for sustainable digital transformation. With the MDM market growing at a steady pace globally across retail, manufacturing, healthcare, government, telecommunications, media and entertainment, financial services, utilities, and logistics, SAP MDG's proven enterprise architecture delivers operational efficiency and regulatory compliance at scale. SAP MDG governance processes, role-based accountability, audit trails, and sophisticated workflows deliver meaningful productivity, cost, and quality improvements that distinguish enterprises from their competitors. The convergence of SAP MDG's capabilities with emerging technologies enables organizations to harness their data assets for digital transformation while ensuring data integrity, data security, regulatory compliance, and data quality across complex enterprise environments.
Multi-Dimensional Expert Matching in Enterprise Technical Routing Systems: A Weighted Graph-Based Approach to Intelligent Resource Allocation
Introduction: The allocation of specialized technical expertise to enterprise service requests constitutes a combinatorial optimization problem of considerable practical significance, yet existing systems overwhelmingly rely on rudimentary heuristics keyword matching, round-robin distribution, or manual triage that disregard the multidimensional nature of expert competency, geographic constraints, linguistic requirements, and stochastic workload fluctuations. Objectives: This article formalizes the enterprise expert-matching problem as a weighted bipartite graph optimization over heterogeneous attribute spaces, introducing a unified scoring framework that jointly optimizes across six orthogonal matching dimensions. Methods: This article introduces the Composite Affinity Score(CAS), a parametric objective function that integrates skill-domain alignment, Technical Field Community (TFC) membership hierarchies, sales territory congruence, language capability vectors, resolver availability indices, and customer-value priority weights. This article presents MDEM (Multi-Dimensional Expert Matching), a greedy approximation algorithm with provable (1 − 1/e)-competitive ratio guarantees under submodular objective conditions, achieving sub-second assignment latency for annual request volumes exceeding 160,000. Results: Empirical evaluation on seven years of anonymized operational data from a global cloud infrastructure provider (N = 1,043,217 requests, K = 12,847 unique resolvers) demonstrates that MDEM reduces mean time-to-resolution (MTTR) by 34.7% relative to manual routing (ρ < 0.001) and 21.9% relative to single-dimension automated baselines while maintaining resolver workload Gini coefficients below 0.12. Conclusions: The framework's generalizability is validated through cross-domain transfer experiments in healthcare specialist referral and legal expertise allocation, yielding MTTR reductions of 28.3% and 19.6%, respectively, confirming that the six-dimensional MDEM structure transfers effectively beyond its originating cloud services context.
Human-Centered Design of Generative AI Systems for Personalized Media and Enterprise Products
Generative artificial intelligence is increasingly embedded in personalized media platforms and enterprise products, yet many systems remain optimized for technical performance rather than human experience. This study examines how human-centered design principles can be systematically integrated into generative AI systems to enhance personalization, usability, trust, and ethical alignment. Using a mixed-methods research framework, the study evaluates generative AI prototypes across media and enterprise contexts by combining user interaction metrics, experiential assessments, and governance-oriented indicators. The results demonstrate that deeper personalization and context-aware generation significantly improve perceived usefulness, interpretability, and user trust while reducing cognitive workload. Visual and statistical analyses further reveal a synergistic interaction between personalization depth and contextual awareness, highlighting their combined influence on sustained engagement and acceptance. Ethical transparency and explainability emerge as critical enablers of adoption, reinforcing the importance of embedding governance mechanisms directly into system design. Overall, the findings establish a human-centered design framework that supports the development of generative AI systems that are not only intelligent but also trustworthy, adaptive, and aligned with diverse user needs in media and enterprise environments.
Sequential Hypothesis Testing for Safe Production Migration of Non-Deterministic Analytical Agents in Latency-Critical Serving Infrastructure
The deployment of updated large language model-based analytical agents in production environments presents a statistically and operationally complex challenge that conventional software deployment strategies are fundamentally ill-equipped to address. Unlike deterministic systems, where a single test request can expose a defect, stochastic analytical agents require statistical aggregation over many observations to detect quality differences between versions. Existing deployment practices, including canary releases, blue-green deployments, and fixed-window A/B testing, each suffer from distinct failure modes: population confounding, binary risk exposure, or temporal inefficiency. This article proposes a deployment framework that adapts dual-write and dual-read consistency patterns from distributed database migration to the domain of non-deterministic agent serving, integrating them with Wald's Sequential Probability Ratio Test to provide formal statistical guarantees on migration decisions. The framework introduces a Confidence-Gated Migration Protocol that evaluates agent quality across four dimensions, analytical correctness, latency profile, semantic equivalence, and safety, using parallel sequential tests with familywise error control. Phased migration through shadow evaluation and canary deployment, governed by site reliability engineering error budget principles, constrains quality assessment to formal error bounds across all evaluation phases. Simulation results indicate a ninety-six percent rate of detecting regressions, a forty-one percent reduction in mean time to safe migration, and a false alarm rate of less than four percent.
Sustainable and Green Software Architecture: Principles, Frameworks, And Emerging Research Directions
The rapid expansion of digital infrastructure and software-intensive systems has significantly increased the energy consumption and environmental footprint of information and communication technologies (ICT), catalysing the emergence of sustainable and green software architecture as a critical engineering discipline. This article offers an extensive academic examination of the fundamental concepts, architecture, performance metrics, and methodologies involved in the development of environmentally sustainable software systems. Drawing from recent peer-reviewed literature in sustainable computing and green software engineering, the study examines how architectural decisions, including service granularity, workload scheduling, and resource management, directly shape environmental outcomes across the software lifecycle. This article analyzes a multi-dimensional approach to sustainability involving environmental, economic, social, and technical aspects along with conventional reference models and green architectural approaches such as microservices, serverless architectures, edge computing, and data reduction techniques. The researchers analyze important performance metrics and findings associated with current issues, especially the lack of sustainability metrics. Attention is drawn to future research directions based on sustainable practices such as carbon-aware cloud computing, artificial intelligence-based energy management, sustainable AI systems, and circular computing. It is noted that incorporating sustainable concepts into software architectures is vital for environmentally sustainable computing environments.
Hybrid Insider Threat Detection Using Adversary Emulation, Endpoint Forensics, and Unsupervised Machine Learning
Internal threats are a big security problem. When internal users perform malicious activities, they use their legitimate privileges to perform legitimate-looking activities. There is not much classified data to analyze. Most detection methods use synthetic data or supervised learning, which makes it difficult to detect the actual behavior of the attacker. This study proposes a hybrid framework for detecting insider threats that combines attack simulation, endpoint forensic analysis, and unsupervised machine learning. Attacks are simulated using the MITRE CALDERA platform, forensic data is collected via Velociraptor, and user behavior is analyzed using Isolation Forest and AutoEncoder models to detect abnormal activity without labeled data. The analysis results are integrated into a unified risk index ranging from 0 to 100 and classified according to CERT criteria to facilitate interpretation of the results. Experiments demonstrate that the proposed framework can detect many types of internal attacks, including abuse of authentication, depletion of system resources, covert execution, and log tampering, making it a practical and applicable solution in the environments of real- world security.
Systematic Integration of Economic Factors in Manufacturing: Evaluating India Against China
An index called the Manufacturing Competitiveness Index (MCI) has been developed and used in the comparison of manufacturing competitiveness between India and China in the period 2016-2024. The index comprises three equally weighted variables – Labour Costs and Productivity, Manufacturing Scale, and Trade and Manufacturing Quality derived solely from the World Bank World Development Indicators. Each variable comprises one to three sub-variables, standardized on a range from 0 to 100 based on the min-max method and summarized to provide a single score for comparison. The analysis has demonstrated that there was a notable reconfiguration of the manufacturing competitiveness of India and China during the period. In 2016, China was more competitive than India, which scored 34.80 and 20.23 respectively. By 2024, India overtook China, earning an index score of 76.62 to China's 63.09, mainly due to India's spectacular growth in Trade and Manufacturing Quality (+97.18 points) and Manufacturing Scale (+94.36 points). The gap has been rapidly closing since 2020, amid the ongoing U.S.- China trade war, disruption of global supply chains by COVID-19, and Make in India Initiative. Accordingly, India is now seen not just as an alternative manufacturing base to China but a legitimate competitor for it. This paper aims to introduce a replicable index measuring manufacturing competitiveness into the existing literature and offer insights for multinational investors, policymakers, and academic researchers working on the global manufacturing sector.
Imperial Study on the Comparative Performance Evaluation of ESG Mutual Funds and Normal Mutual Funds in India
Mutual funds are professionally managed investment vehicles that pool money from multiple investors to invest in diversified portfolios of financial securities such as equities, bonds, money market instruments, and other assets. Managed by Asset Management Companies (AMCs) like SBI Mutual Fund and HDFC Asset Management Company, mutual funds aim to generate returns in line with specific investment objectives, such as capital appreciation, income generation, or capital preservation. Investors in mutual funds are allotted units based on the Net Asset Value (NAV), which represents the per-unit value of the fund’s holdings. The NAV fluctuates according to changes in the market value of the underlying assets. Mutual funds offer diversification, professional management, liquidity, and accessibility, making them suitable for both small and large investors. They are broadly categorized into equity funds, debt funds, hybrid funds, and index funds, each varying in risk and return profile. Due to regulatory oversight, transparency, and ease of investment through mechanisms like Systematic Investment Plans (SIPs), mutual funds have become a popular investment avenue for long-term wealth creation and financial planning. Overall, mutual funds serve as an efficient financial intermediary that connects individual investors to capital markets while managing risk through diversification and professional expertise.
Policy-Driven Frontend Governance in Regulated Financial Enterprises
In regulated financial ecosystems, financial services providers are increasingly facing the challenge of balancing growing throughput and the requirement for speed in delivering digital services with evolving regulations. Frontend systems, as the primary point of interaction between regulated entities and end users, are thus at the epicenter of this challenge. While frontend layers are fundamental to regulated financial systems, their systematic governance within platform ecosystems has too often received less attention than backend infrastructure or data governance. We argue that policy-driven frontend governance is an important architectural and organizational property of regulated financial platforms. Leveraging existing research in software information systems development, security and privacy engineering, and digital platform governance, we contribute a framework for embedding compliance behaviors in front-end systems. Our framework consists of patterns of composable elements, risk-based review, automation-based conformity, governance-informed architecture, and the organizational and cultural capabilities to support frontend governance at scale. By presenting a framework for frontend governance, we contribute to the information systems literature by viewing frontend governance as a first-class concern, complementing existing work on backend governance and infrastructure governance.
Engineering Data Quality in Healthcare Systems: A Proactive Testing Approach for Medicare Compliance
Healthcare systems supporting Medicare operate within highly regulated environments where data quality directly influences compliance, financial accuracy, and service delivery. Conventional methods treat data validation as a secondary process, which does not account for the complexities of modern distributed data structures. This article considers data quality as an essential engineering tier that spans the entire data life cycle. In this approach, the validation process is designed in order to comply with requirements, including requirements of completeness, traceability, and auditability. Reactive data testing methods present limitations, such as delayed identification of problems, inability to validate data transformations, and scalability challenges when using cloud-based systems. In response to these weaknesses, a new proactive architecture for data quality engineering is introduced, which includes source-level validation, transformation-level validation, and reconciliation across levels. The transition of quality assurance from a process to a strategic function is established. The implementation of data quality as an architectural component significantly improves the performance of error identification and auditing and increases system reliability. Therefore, it becomes a necessary condition for healthcare data management.
Psychological Capital and Digital learning behaviour: A Meta-Analysis of Global Antecedents and Implications for South African Higher Education
Introduction: Psychological Capital (PsyCap) has been shown internationally to enhance digital learning-related behaviours (DLB); its role in Higher Education Institutions (HEIs) is underexplored and theoretically disintegrated. This study synthesises global empirical evidence to generate a contextualised framework that informs capacity development in South African HEIs. Objectives: This meta-analysis aims to synthesise global research evidence on PsyCap and workplace digital learning, identify key antecedents and effect sizes, and examine their relevance to the South African HE context. Methods: Five databases (PsycINFO, Scopus, Web of Science, ProQuest, and PubMed) were systematically searched using meta-analysis to identify peer-reviewed empirical studies (2020-2025). Inclusion criteria were operationalised using the PICOS framework. Results: Using Comprehensive Meta-Analysis Software v4, 30 effect sizes revealed that PsyCap demonstrated a moderate positive association with workplace learning (r = 0.36), with significant effects for informal DLB (r = 0.35) and academic/learning outcomes (r = 0.39). Moderator analyses showed that PsyCap effects were amplified in global empirical studies, adaptive behavioural, and supportive organisational climates within educational sectors. Thematic analysis acknowledged four recurring mechanisms: (i) PsyCap consolidates reflective DLB, (ii) HERO dimensions moderate persistence during institutional turbulence, (iii) organisational climate moderates the activation of PsyCap, and (iv) adaptive behaviours moderate the transformation of psychological resources into learning participation. Conclusions: For South African HEIs, the results suggest that enhancing PsyCap can help mitigate the effects of chronic change fatigue, resource imbalances, inconsistent developmental trajectories, and digital transformations. Integrating PsyCap-building micro-interventions and psychologically supportive climates into HRD, academic development, and leadership programmes to enhance staff and student learning cultures.
Assessing Climate-Related Behavioural Engagement in the Himalayan Region of India
Introduction: The Himalayan region, known for its natural heritage and unique biodiversity, is particularly vulnerable to the impacts of climate change. From this perspective behavioral engagement emerges as a crucial factor, as individual and collective actions significantly influence climate adaptation, resilience and sustainable environmental practices in the region. Objectives: This research paper aims to assess the climate-related behavioral engagement among population, considering Preservation and utilization factors that influence their actions. Methods: The researcher employed a 2-MEV scale to collect data from 142 respondents, from different parts of Uttarakhand, representing a diverse cross-section of the population. Results: Key findings revealed a noteworthy level of engagement among the respondents regarding climate change issues, with 50 percent respondents reported to agree to indulge in some or other kind of climate related actions. Conclusions: This study provides insights into their adaptation strategies and broader understanding of climate resilience, sustainable development and environmental conservation.
XR-Enabled Multimodal Interaction for Sustainable Transmission of Intangible Cultural Heritage: A Study on Public Emotional Engagement and Pro-Protection Behavioral Intentions
Intangible Cultural Heritage (ICH) serves as an irreplaceable repository of cultural genes, historical memories, and national identities, underscoring its significance in fostering cultural diversity and intercultural dialogue. However, rapid globalization, digital transformation, and shifting societal lifestyles pose formidable challenges to ICH, including intergenerational discontinuity, limited public participation, and geographical/temporal constraints on dissemination. Extended Reality (XR), integrating Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (MR), offers innovative solutions via its immersive, interactive, and multi-sensory capabilities. This study explores how XR-enabled multimodal interaction influences public pro-protection behavioral intentions toward ICH, focusing on the mediating role of emotional engagement and moderating effects of perceived authenticity and place attachment. Based on the Stimulus-Organism-Response (S-O-R) framework, Theory of Planned Behavior (TPB), and affective affordances, a theoretical model was tested using a mixed-methods design.Results reveal XR multimodal interaction significantly predicts emotional engagement but has no direct effect on pro-protection intentions. Emotional engagement fully mediates this relationship, with cultural identity exerting a stronger effect than emotional resonance. Perceived authenticity and place attachment positively moderate the emotional engagement-intention link. This study enriches digital heritage research and provides actionable insights for cultural heritage management, technology development, and policy formulation.
Introduction: The significance of AI infrastructure lies in its ability to accelerate innovation while maintaining operational resilience. Efficient infrastructure reduces model training time, optimizes resource utilization, and enables real-time inference at scale. It also supports advanced workloads such as generative AI, large language models, computer vision, and autonomous systems. Beyond performance, modern AI infrastructure integrates governance, security, and compliance mechanisms to ensure responsible AI usage and data protection.
Leveraging Deep Learning for Anomaly-Based Intrusion Detection in Internet of Things Networks: An LSTM Approach with Recursive Feature Elimination
Background: The accelerating deployment of Internet of Things (IoT) devices has enlarged the cyber-attack surface of contemporary networks, while the resource-constrained nature of typical IoT endpoints precludes the use of conventional, signature-based defences. Anomaly-based Intrusion Detection Systems (IDS) powered by deep learning offer a promising alternative, but high-dimensional traffic data and class imbalance continue to limit their accuracy and inference speed in production environments. Objective: This study designs, implements and evaluates an anomaly-based Network IDS for IoT environments that combines Recursive Feature Elimination (RFE) for dimensionality reduction with a stacked Long Short-Term Memory (LSTM) classifier. The work investigates whether feature selection prior to LSTM training can preserve detection performance while reducing computational cost, and whether the resulting pipeline generalises across heterogeneous IoT traffic captures. Methods: We evaluated the proposed pipeline on two publicly available benchmarks (IoTID20 and TON-IoT) covering both binary (attack vs. normal) and multi-class classification of intrusion categories. After data cleaning, normalisation and label encoding, RFE with a Decision Tree estimator selected 23 features for binary tasks and 32 features for multi-class tasks. A two-layer LSTM (108 and 64 units) with dropout regularisation was trained for five epochs using the Adam optimiser, with Model Checkpoint and Early Stopping callbacks. Performance was measured with precision, recall, F1-score and accuracy, supported by confusion-matrix analysis. Results: On both datasets the model achieved 100% accuracy in binary classification, with only nine and thirty-nine misclassifications across IoTID20 (51,780 test instances) and TON-IoT (87,162 test instances), respectively. For multi-class detection the overall accuracy reached 97% on both datasets, with per-class F1-scores ranging from 0.80 to 1.00. The proposed RFE+LSTM model matched or exceeded comparable deep-learning baselines reported in the literature while operating on substantially fewer features. Conclusion: Coupling RFE-based feature selection with an LSTM classifier yields a lightweight yet highly accurate anomaly-based IDS that generalises across two structurally different IoT datasets. Future work will address adversarial robustness, real-time deployment and adaptation to streaming, concept-drifting traffic.
Design And Optimization of a Thermal Management System for a Compact Crossover Electric Vehicle
Introduction: This study presents an innovative approach to Battery Thermal Management Systems (BTMS) for electric vehicles, specifically focusing on compact crossover designs. We propose a novel hybrid cooling system that integrates liquid cooling channels with metal foam heat dissipation. The system incorporates strategic placement of cooling passages and intelligent control mechanisms for optimal temperature regulation. Through Computational Fluid Dynamics (CFD) analysis, we demonstrate that our proposed system maintains battery temperature within optimal operating ranges (25-45°C) even under extreme conditions. Results show the system achieves an average temperature of 35°C when ambient battery temperature reaches 60°C, indicating effective thermal management capability. The paper presents the design and optimization of an innovative thermal management system for electric vehicle (EV) battery packs. The proposed design features a multi-chamber battery structure with integrated cooling channels, specifically engineered for compact crossover vehicles. The system incorporates rectangular cooling passages between battery chambers, offering enhanced thermal dissipation compared to conventional cylindrical cooling tubes. Computational fluid dynamics (CFD) simulations validate the thermal performance of the design, demonstrating improved heat transfer characteristics through the rectangular cooling channels. Conclusions: The proposed thermal management system represents a significant advancement in EV battery cooling technology. The system's ability to maintain average temperatures of 35°C under extreme conditions (60°C) validates its effectiveness for practical applications in compact crossover electric vehicles[5]. The multi-chamber design with rectangular cooling passages demonstrates superior thermal performance while maintaining practical packaging dimensions for compact crossover vehicles. The integration of active and passive cooling mechanisms, combined with intelligent control systems, provides an effective solution for EV battery thermal management. This paper presents a significant advancement in electric vehicle battery thermal management through the development of an innovative multi-stage cooling and heating system. The proposed design successfully addresses several critical challenges in EV battery technology: The successful implementation of this thermal management system demonstrates significant potential for improving electric vehicle performance and reliability. The modular design approach ensures adaptability to future battery technologies while maintaining robust thermal control [9]. As electric vehicle adoption continues to grow, the innovations presented in this research will contribute to the development of more efficient, safer, and more reliable electric vehicles. The demonstrated results suggest that this system could become a standard solution for next-generation electric vehicles, particularly in regions with extreme climate conditions [17]. Continued research and development in this field will further enhance the capability and efficiency of electric vehicle battery systems, supporting the global transition to sustainable transportation.
Assessing Soundscape and Urban Noise Pollution: A Case Study of Liberty Square (Church Square) in Skikda, Algeria
The Liberty Square in Skikda is a central urban space characterized by heavy traffic (more than 283 cars per 15 minutes on Av Didouche Mourad, next to the Liberty Square, during peak hours) and social activity. This situation generates noise pollution that can affect users' comfort and detract from the heritage environment. In this context, the present study aims to characterize the square's soundscape using an approach that combines in situ measurements and digital modeling. Acoustic surveys were conducted using a sound level meter at several strategic points within the square, allowing sound pressure levels to be measured in dB(A). These measurements were then compared with the results of a simulation conducted with Olive Tree Lab software, yielding a detailed sound map. The analysis reveals that the highest levels (up to 80 dB(A)) and above are concentrated along the road axes, confirming the dominant role of motor traffic as a source of nuisance. The central area, on the other hand, has moderate levels due to sound dispersion, which is favored by the square's openness. The study concludes that mitigation measures are necessary, including the integration of green barriers, the creation of pedestrian areas, and the reorganization of traffic flows, to improve the sound quality and quality of life in this iconic space.
Significance of Artificial Intelligence in Harmonizing Enterprise Resource Planning Systems
AI as an embedded utility in enterprise resource plannig refers to a shift in the model of managing the business processes of modern companies. Companies face disparate processes, incoherent data models, and siloed operational modules. Process knowledge is useful corporate knowledge that can improve organizational performance. Information fragmentation occurs when different sources contain information expressed in different representation formats. Stack fragmentation issues are addressed by an automated fragmentation detection and resolution mechanism. The enterprise architecture enables embedding of artificial intelligence technologies in core business processes, as demonstrated by platforms such as SAP S/4HANA and SAP Business Technology Platform (BTP). Cloud-native environments — including Microsoft Azure, Amazon Web Services (AWS), and Google Cloud Platform — provide the scale required to run artificial intelligence workloads, while DevOps practices allow for artificial intelligence to be deployed quickly through continuous integration and delivery (CI/CD). SAP Joule, SAP's generative AI copilot, exemplifies how prompt engineering improves the enterprise capabilities of an artificial intelligence model. Greenfield implementations maximize data transparency, as organizations can build new capabilities during the design process. Brownfield transformation requires assessment of the existing data landscape, considering artificial intelligence processing criteria. Software automation ultimately increases the efficiency and reliability of the industrial productivity landscape. User-facing internal tools see the largest efficiency gains from artificial intelligence, and administrative dashboards see the most substantial increase in user satisfaction.
Smart Workflow Orchestration at Kaiser Permanente: An Event-Driven Microservices Approach to Healthcare Integration
Despite the advantages of these technologies, challenges remain due to interoperability issues, redundant activities in the clinic, and workflow delays. The implementation of electronic health records, pharmacy management systems, and telemedicine systems can help healthcare organizations avoid redundancy, streamline document management, and improve operational efficiency. Event-driven microservices allow asynchronous communication and provide scalability and real-time response. With the help of the orchestration layer, healthcare providers can optimize the process of care coordination by automating scheduling, reducing duplicate test orders, and improving the accuracy of clinical records. Additionally, a modular architecture makes the system highly resilient since separate modules can be developed independently without affecting other modules' performance. The proposed workflow orchestration framework aligns with healthcare digital transformation goals, focusing on interoperability, scalability, and patient-centered care delivery.
DTS-Swarm: Cross-Modality Policy Distillation for Robust Multi-UAV Target Tracking under Degraded Sensing
Learned multi-UAV tracking policies often perform well in simulation but degrade when deployed under noisy, range-limited sensing. This paper presents DTS-Swarm, a distilled teacher-student transfer framework for robust multi-UAV target tracking. The teacher observes privileged simulator state, whereas the deployable student acts from a horizontal probabilistic occupancy map, its own kinematic state, and compact teammate-relative features. The method combines privileged teacher training, partial decoder-layer transfer, temperature-annealed Kullback-Leibler policy distillation, and a weak V-formation auxiliary loss. Across five degraded-sensing scenarios, evaluated over 30 episodes per seed and 5 random seeds with seed-level confidence intervals, DTS-Swarm reduces nominal target-wise tracking error by 32.6% relative to a no-transfer student and reduces high-noise tracking error by 44.1%. The main empirical finding is that hidden-layer transfer can produce negative transfer when action-distribution alignment is removed. In this cross-modality setting, copied teacher weights become useful only when KL distillation aligns the teacher and student action distributions during noisy-map fine-tuning.
A Comparative Survey of Smart City Traffic Management Solutions: Taxonomy, Tools, and Emerging Trends
Artificial intelligence (AI) has become a critical component of modern urban environments, driving the development of smart cities that promise enhanced efficiency, sustainability, and improved quality of life. Despite these advancements, traffic congestion remains a significant challenge in urban mobility. Addressing this challenge requires further investigation to develop innovative and effective solutions. This paper reviews recent research on traffic management in smart cities, providing a comprehensive analysis of current strategies. It emphasizes the need for adaptive, integrated approaches to traffic control in the context of rapidly evolving urban systems.
Evaluating ISO Standards in Blockchain. A systematic Review of Africa.
Introduction: Blockchain technology offers a promising foundation for secure, transparent, and decentralised data sharing across sectors such as healthcare, finance, education, and governance. To support its responsible adoption, ISO/TC 307 provides standards for blockchain terminology, privacy, architecture, and interoperability. However, the relevance and applicability of these standards remain underexplored in African contexts, where infrastructural, legal, and institutional conditions often differ from those assumed in global frameworks. This study systematically evaluated the suitability of key ISO blockchain standards for secure data sharing in Africa, drawing on PRISMA-guided evidence from 30 studies published between 2018 and 2025. Objectives: This study evaluates ISO standards related to blockchain-based data sharing, highlighting their strengths, limitations, and alignment with the need of developing regions, especially Africa. Methods: A systematic literature review (SLR) was carried out, focusing on peer-reviewed studies published between 2018 and 2025. Databases included IEEE Xplore, SpringerLink, ScienceDirect, and Taylor & Francis Online. The PRISMA framework guided the screening process, yielding 30 final studies for thematic analysis. Results: Standards such as ISO 22739, ISO/TR 23244, and ISO/TS 23258 provide foundational guidance on terminology and privacy but lack detailed implementation support for decentralised, low-resource settings. Adoption in Africa is limited by regulatory misalignment and insufficient sector-specific frameworks. Conclusions: ISO blockchain standards require refinement with clearer, context-aware guidance to meet Africa’s unique infrastructural and legal needs. The study contributes theoretical and practical insights to improve ISO standard design and adoption for secure data sharing in developing regions.
Deep Learning-Based Network Traffic Analysis For Intrusion Detection In Cyber Security Systems
The development of internet-based applications has led to a more complex cyberspace, a major challenge to the cybersecurity experts in the development of intelligent real-time cybersecurity tools. In this paper, we propose a very effective intrusion detection model based on a hybrid GRU + BiLSTM on the BoT-IoT dataset. The suggested method combines data preprocessing, feature selection through Chi-square method, normalization, and data balancing through SMOTE to optimize model performance. The combination of GRU and BiLSTM allows for a model that detects malicious actions at extremely high rates by successfully capturing bidirectional and temporal patterns in network data. Experimental findings show high performance, with 99.1% precision, 99.3% F1-score, and 99.7% ROC-AUC, which show the good capability of classification and high robustness. The training and validation trends also indicate that the model has good generalization with little overfitting. The proposed approach is more effective than traditional and deep learning methods compared to the current models. In general, the research establishes that hybrid deep learning models are well fit to detect intrusion in IoT settings, and they offer effective and cost-effective security measures.
Improved Electrical Efficiency and Thermal Efficiency Thanks to Active Cooling by Hybrid Nano Fluid for an Organic Cell Based on PTB7: PC70BM
This work investigates the effect of daily thermal cycling on organic solar cells (PTB7:PC₇₀BM). The effectiveness of this system is evaluated by using a liquid-cooled nanohybrid to improve device performance and durability. Two scenarios are compared: one without cooling and one with active cooling. The results demonstrate that cooling increases the thermal efficiency and electrical stability, while reducing degradation of organic solar cells. Based on this, a structural analysis of the material was performed to study the effects of conductivity dynamics, operating temperature, and series resistance. This approach determines the optimal conditions for efficient operation. Furthermore, cooling organic solar cells using a liquid-cooled nanohybrid contributes to extending the cell lifetime by mitigating the thermal effects of the operating environment. Overall, the results highlight the high sensitivity of organic solar cells to thermal stress and emphasize the importance of proper thermal control to ensure efficient and excellent performance over a long period of time.
Amelioration of Automation Techniques in AutoML
AutoML is used in this project to address the limitations of traditional machine learning (ML), which often requires expert knowledge for model selection, tuning, and validation. By automating these processes, AutoML makes data analysis more efficient and accessible to a broader audience, including non-experts. This research proposes an automatic learning machine (AutoML) system that enables users to access detailed data analysis, predictive modeling, and visualization by exporting data into Excel format. Based on the features of the input data, the platform automatically chooses the best machine learning algorithm and facilitates group learning to increase prediction accuracy. A larger audience can utilize the system due to its easy-to-use interface, which allows non-experts to perform advanced data analysis effortlessly.
Are LLMs Reasoning or Ranking? Diagnosing and Mitigating Order Bias in Knowledge Graph Completion
Introduction: Knowledge graphs power modern question-answering, recommendation, and entity-aware language models, but are perennially incomplete. The task of knowledge graph completion (KGC) infers missing triples and is increasingly approached by finetuning a large language model (LLM) to select the correct entity from a candidate list produced by a lightweight embedding retriever. Such systems report strong Hits@1 improvements over the retriever and are interpreted as evidence that the LLM reasons about which candidate matches the query. Objectives: This paper tests that interpretation directly. We ask two questions. First, are LLM-based discrimination rerankers actually reasoning about candidate identity, or are they exploiting candidate position in the prompt as a shortcut? Second, if the shortcut is present, can it be removed by a simple change to the training procedure? Methods: We finetune Mistral-7B with QLoRA on FB15K-237 candidate lists produced by a TransE retriever under two training regimes: candidates always in retriever-score order, and candidates independently shuffled in every batch. Each trained model is evaluated under three inference-time orderings of the same candidate set: identity, uniform random permutation, and adversarial reversal. Differences across orderings isolate the reranker's dependence on candidate position rather than candidate content. Results: The ordered-trained reranker reaches Hits@1 of 0.310 in distribution but collapses to 0.059 under shuffled inference and to 0.011 under reversal (below the 0.05 chance level of guessing uniformly in the candidate list), with residual accuracy falling below the TransE retriever it builds on. The shuffled-trained reranker is essentially flat across the three conditions (0.249, 0.247, and 0.244 Hits@1), reducing the across-condition spread by a factor of about sixty at the cost of approximately six absolute Hits@1 points. Conclusions: Apparent reasoning by LLM-based KGC rerankers is largely an artifact of candidate-position exploitation. Once the shortcut is blocked, the LLM contributes little beyond the retriever it sits on top of. Robustness to candidate ordering should be a routine evaluation criterion for any LLM-augmented retrieval system, and the framing of such systems as reasoning models should be tempered with a corresponding control.
Training-Budget Sensitivity of Method Rankings in Safe Reinforcement Learning: A Case Study on Quadrotor Control Under Wind Disturbance
Empirical comparisons in safe reinforcement learning (RL) are usually reported at a single, fixed training budget, and the resulting method ranking is then treated as a property of the algorithms. We show that on a quadrotor hover task under wind disturbance this ranking is instead strongly budget-dependent. Using two runs of an identical five-method pipeline—an unconstrained PPO baseline and four Lagrangian variants spanning a two-by-two factorial of error signal (mean cost versus CVaR) and dual-update rule (gradient ascent versus PID)—we find that in calm air the family of PID-controlled Lagrangian methods ranks last at 500K steps and first at 2M steps, while the unconstrained baseline moves in the opposite direction. Quantified by Spearman rank correlation between the two budgets, the ordering is negatively correlated at moderate wind (rho of minus 0.9, p of 0.037) and, pooled across all five wind conditions, significantly negative overall (rho of minus 0.4 over 25 method-condition cells, p of 0.048): methods that look better at 500K tend to look worse at 2M. Because the two runs differ in budget, seed count, and—for the CVaR cells—an adaptive cost-limit calibration introduced between them, we isolate the budget effect two ways that are immune to the calibration confound: (i) the periodic in-training logs of the 2M run, which compare 500K and 2M on the same 20 seeds with calibration held constant, and (ii) the mean-cost PID-Lag method, which uses no CVaR calibration yet still rises from the bottom at 500K (58.8) to the top at 2M (257.7). Bootstrap confidence intervals confirm the reversal. We further show that more compute is not uniformly better: under moderate and strong wind several methods lose return between 500K and 2M, and the safety (violation-rate) ranking shifts with budget as well. Finally, the two top methods at 2M are statistically indistinguishable (paired Wilcoxon, n of 20, corrected p of 1.0; effect size of 0 at the strongest wind), so the phenomenon is a property of the PID family, not of any single method. We argue that safe-RL benchmarks should report convergence evidence (learning curves) and budget sensitivity before publishing a ranking, and we give a concrete checklist. The 2M-step, 20-seed run re-analyzed here is the converged study reported in our companion paper (Chaouli and Elbar 2026b); all findings are recomputed from raw logs and no new training was performed.
Hybrid Wavelet and K-Means Clustering Framework with Bilateral Filtering for Automated COVID-19 CT Lesion Segmentation
Coronavirus disease (COVID-19) has emerged as a major global health challenge, making early and accurate diagnosis crucial, particularly for asymptomatic patients. Computed Tomography (CT) imaging has proven to be an effective modality for detecting COVID-19-related lung abnormalities. In this paper, a hybrid framework integrating Wavelet Transform (WT), K-Means Clustering (K-MC), and Bilateral Filtering (BF) is proposed for automated segmentation of COVID-19 lesions in chest CT images. Initially, WT is employed for image denoising in the frequency domain to suppress noise while preserving structural details. Subsequently, K-Means clustering based on texture characteristics and local gray-level entropy is applied to achieve automatic segmentation of infected regions. The obtained segmentation results are further refined using bilateral filtering to reduce residual noise and preserve edge information. Finally, morphological post-processing operations are performed to improve segmentation accuracy and region consistency. Experimental results obtained on a publicly available COVID-19 CT dataset demonstrate that the proposed framework achieves a mean Dice coefficient of 0.8177, a Jaccard index of 0.7322, a sensitivity of 0.8281, a precision of 0.8538, and a specificity of 0.9837. Furthermore, the proposed method outperforms four state-of-the-art segmentation approaches (CV, MAC, FRAGL, and SPF) while reducing computational time by more than 87% compared with conventional methods. These findings highlight the effectiveness and computational efficiency of the proposed framework for automated COVID-19 CT image analysis.
Build-Aware Security Scanning for Modern JavaScript Applications: The SecureBuild Framework
SecureBuild is a security scanning platform that is open source, enabling developers to quickly analyze their modern react and node.js applications to solve the critical security problems of today. Modern web apps rely on elaborate front-end build pipelines, distributed back-end services, and API-dependent architectures, forcing traditional security tools to be outdated in order to be able to uncover vulnerabilities deeply embedded within the application and fully operational misconfigurations. SecureBuild solves these problems by incorporating build-to-source mapping, a static taint-aware analysis, and a light-weight runtime validation into one security analysis framework. The platform has the ability to detect vulnerabilities like Cross-Origin Resource Sharing (CORS) Misconfigurations, Weak Content Security Policies (CSP), SQL Injection Vectors, Unsafe JavaScript Execution Patterns, and Suspicious Automated Bot Activities. SecureBuild uses source mapping to re-map minified frontend bundles to their original source code so that developers can easily trace vulnerabilities and speed up remediation efforts. Moreover, the lightweight runtime validation engine helps in detecting violations with ephemeral runtime checks, HTTP header validation, preflight request analysis, and browser-based CSP violation simulations using automation tools like Playwright and Puppeteer. SecureBuild is optimized for DevSecOps and CI/CD environments to deliver pull-request ready scans with low false-positive rates and confidence-based findings, remediation guidance, SARIF compliant reporting and seamless integration with platforms like GitHub Actions and GitLab CI. In addition to vulnerability detection, the platform also focuses on developer adoption with extensible rule systems and customizable configurations, suppression baselines, and streamlined onboarding to minimize integration friction in existing software development workflows. SecureBuild brings together the capabilities of traditional static analysis tools and the more comprehensive dynamic scanning solutions in a scalable, accurate and workflow-friendly way. The platform allows organizations to detect and fix vulnerabilities before they go into production, reduce production security risks, create developer trust in security tools, and enhance the security of modern web infrastructures.
Data Governance and Privacy in the Age of Cloud Computing Impact of Generative AI on Business Processes- Systematic Review
Background: The adoption of generative AI within cloud-based business environments has accelerated, raising concrete concerns related to data governance, privacy, security, and regulatory compliance. Objective: To systematically review peer-reviewed studies (2020–2024) examining how generative AI influences data governance, privacy protection, and business process management in cloud settings. Methods: Five databases (IEEE Xplore, ACM, SpringerLink, ScienceDirect, and arXiv) were searched for studies published between 2020 and 2024. From 2,500 records screened using PRISMA criteria, 20 studies met the inclusion requirements. Data were analyzed through thematic synthesis and descriptive reporting of study characteristics. Results: Six themes were identified: data governance and privacy (12/20, 60%), security challenges (11/20, 55%), business process applications (10/20, 50%), ethical and legal issues (9/20, 45%), cloud integration (8/20, 40%), and innovation versus regulation (7/20, 35%). Of the included studies, 7/20 (35%) were empirical and 13/20 (65%) were conceptual or theoretical. Adversarial attacks were assessed in 3/20 (15%) studies, while algorithmic bias appeared in 8/20 (40%), indicating uneven coverage of core risk areas. Conclusion: Based on the findings, three key recommendations are proposed. First, organizations should adopt adaptive AI governance frameworks that integrate privacy, security, and compliance mechanisms across cloud environments. Second, investment in empirical security testing particularly for adversarial robustness and data leakage prevention is essential. Third, policymakers should develop sector-specific regulatory sandboxes to enable controlled experimentation with generative AI systems while ensuring compliance with data protection standards.
IoT-Enabled Digital Transformation of State Tubewell Irrigation Systems: A Case Study of an IoT-Based Pump Monitoring System
The Tubewell East Zone, Ayodhya manages 9,107 government tubewells covering approximately 7.36 lakh hectares of agricultural land. Before this initiative, pump operations were managed manually with no real-time data, leading to frequent motor burn cases, high downtime and poor irrigation reliability. The system uses cloud-connected sensors with M2M SIM-based connectivity to track nine key parameters and generate pre-failure alerts. After deployment, total running hours increased from 1,37,868 to 1,84,637 hours, irrigation coverage grew from 5,945.08 hectares to 8,354.77 hectares and zero motor burn cases were recorded. The estimated economic benefit is approximately Rs. 1.25 lakh per tubewell per year. This study offers a replicable governance model for digital transformation of public irrigation infrastructure in developing economies.
Mixture Generalized-Gamma Distribution for Sea-Clutter and its Parameter Estimation Method
The paper proposes the mixture generalized gamma (G-gamma) model for sea clutter. Such distribution is useful, because the mixture gamma, the mixture Weibull and the mixture Nakagami distributions are special cases. A hybrid estimation method based on integer order moments and simplex search algorithm is derived in order to reduce the shape parameters finding complexity in four dimensions (4D) instead of 7D. To this end, a linear equation of second order is obtained to solve the probability variable as a function of the estimated shape parameters. The scale parameter is then calculated as a function of the first order moment and the estimated probability and shape parameters. The latter are computed by means of the recursive Least Square (LS) method with the simplex search algorithm. Modeling of IPIX (Intelligent PIxel X-bande radar) sea clutter is investigated using both single and mixture G-gamma distributions. The convergence of the proposed hybrid estimation approach is confirmed in terms of several scenes of IPIX database. It is also observed that the mixture G-gamma distribution exhibits the best fit to real data in most cases.
CognitiveTwin-Edge: A Self-Adaptive Digital Twin Framework with Federated Edge Intelligence for Predictive Urban Mobility Orchestration
The exponential growth of connected vehicles, IoT sensors, and smart city infrastructure generates unprecedented volumes of heterogeneous mobility data, creating both opportunities and challenges for real-time urban traffic optimization. Current traffic management systems predominantly operate as reactive systems, responding to congestion after it materializes, while simultaneously struggling with critical limitations in privacy preservation, energy efficiency, and scalability. This paper introduces CognitiveTwin-Edge, a novel framework that synergistically integrates cognitive digital twins with hierarchical federated edge intelligence to enable proactive, privacy-preserving urban mobility orchestration. Unlike existing digital twin implementations that function as passive mirror systems merely replicating current state without anticipatory capabilities, our framework endows digital twins with cognitive properties including autonomous self-configuration, adaptive self-healing, and predictive anticipatory behavior through a novel Temporal-Spatial Attention Mechanism (TSAM). The framework employs a three-tier hierarchical federated learning architecture where geographically distributed edge nodes collaboratively train mobility prediction models without sharing raw sensor data, thereby preserving citizen privacy while enabling city-wide traffic optimization. We introduce the innovative concept of Mobility Intent Graphs (MIGs) that capture latent movement intentions and aggregate demand patterns across urban zones, enabling proactive rather than reactive traffic management through predictive orchestration. The framework incorporates an adaptive synchronization protocol that dynamically adjusts model update frequency based on detected mobility dynamics, reducing communication overhead while maintaining prediction accuracy. Comprehensive experimental evaluation on synthetic datasets modeling a metropolitan area with 500,000 daily vehicle trips across 200 traffic zones demonstrates that CognitiveTwin-Edge reduces average travel time by 23.7% during peak hours, decreases total system energy consumption by 18.4%, and maintains high prediction accuracy (RMSE of 18.3 vehicles/hour at 15-minute horizon) while reducing communication overhead by 67% compared to centralized approaches. The framework provides differential privacy guarantees (ε=1.0) and demonstrates robust scalability from 20 to 200 edge nodes. This work represents a paradigm shift from reactive to anticipatory, centralized to federated, and privacy-compromising to privacy-preserving urban mobility systems.
Hybrid Deep Learning and Local Binary Pattern Model for Masked Face Recognition
Robust face recognition under partial occlusion remains one of the most persistent challenges in computer vision. The COVID-19 pandemic exposed the vulnerability of traditional facial recognition systems to occlusions such as masks, but the problem continues to affect modern biometric applications in healthcare, security, and public environments. This paper introduces a hybrid approach that enhances masked face recognition by combining deep learning features with handcrafted texture descriptors. The proposed method extracts deep features using three pre-trained Convolutional Neural Networks (CNNs)—VGG-16, MobileNet-V2, and Xception—and complements them with Local Binary Pattern (LBP) features to capture fine-grained texture information. Both feature types are concatenated and fed into a Multilayer Perceptron (MLP) classifier for final recognition. Image enhancement and extensive data augmentation are applied to improve robustness and generalization. Experimental results on the Simulated Masked Face Dataset demonstrate that our hybrid CNN–LBP–MLP framework achieves superior accuracy compared to state-of-the-art methods, while maintaining computational efficiency. This study highlights the continued importance of developing reliable face recognition systems resilient to real-world occlusions beyond the pandemic era.
Exploring Citizen Awareness of AI Technologies in Disaster Preparedness and Risk Reduction
This study seeks to explore the level of awareness, perceptions, trust, and concerns of citizens about the application of Artificial Intelligence (AI) technologies in disaster preparedness and risk reduction. With the help of a descriptive quantitative approach, the data were collected from 100 respondents across cities and municipalities across the country through an online survey. The results show that while the citizens demonstrate awareness of both disaster preparedness and knowledge in AI concepts, their perception of AI’s effectiveness in disaster prediction and response remains neutral. The respondents express uncertainty about AI’s capability to produce accurate, reliable, and life-saving capabilities, resulting in the least trust and limited acceptance of AI-generated disaster result information. The issues and concerns include data privacy, technological failure, and the necessity of maintaining human intervention in AI-based systems. Overall, the study highlights a gap between trust and public awareness. Featuring the importance of transparency, proper communication, and community-level education to improve public confidence in AI-supported disaster management systems.
Multiscale Computational Design of Ti-6Al-4V/SiC Composite Femoral Stems: SIMP Topology Optimisation, Functionally Graded TPMS Lattices
Aseptic loosening driven by stress shielding remains the principal long-term failure mode of total hip arthroplasty (THA), affecting 10-15% of implants within 15 years and generating annual revision costs exceeding USD 8 billion globally. This study presents an integrated multiscale computational framework for the design of functionally graded femoral stems using Ti-6Al-4V/SiC whisker-reinforced composite materials. The framework systematically couples seven sequential modules: (M1) Halpin-Tsai micromechanical characterisation of three material systems Ti-6Al-4V, Ti/SiC 20 vol%, and Ti/SiC 40 vol%; (M2) analytical beam stress analysis under ISO 7206-4 oblique cantilever loading; (M3) SIMP topology optimisation at V* = 0.70 achieving 30% mass reduction (C/C₀ = 0.37), (M4) TPMS lattice homogenisation for four architectures (Gyroid, Diamond, Cubic, Octet) with a corrected stress shielding index (SSI_Gyroid = +11.1%) representing an 87% reduction from monolithic Ti-6Al-4V (SSI = 82.5%), (M5) multiaxial fatigue analysis combining Rainflow cycle counting, modified Goodman criterion, and dual Miner damage reporting (D_ISO = 0.001-0.018; D_full = 0.117-1.426. The Ti/SiC 20 vol% composite in a functionally graded Gyroid architecture emerges as the optimal clinical configuration, uniquely satisfying all biomechanical, osseointegration, and SLM fabricability requirements simultaneously.
Intelligent Time-Off Recoupment: An ERP-Integrated System for Multi-State Payroll Compliance
Introduction: Many organizations allow employees to use paid time off (PTO) before they have fully earned it. This helps improve employee satisfaction and retention. However, when employees leave the organization with a negative PTO balance, employers face challenges recovering the overpaid amount while following federal and state payroll laws. Objectives: This paper introduces the Intelligent Time-Off Recoupment System (ITORS), a framework designed to automate the recovery of advanced PTO. The goal is to improve payroll compliance, reduce financial losses, and create a consistent process for managing PTO recoupment across different states. Methods: This study uses a design science research approach to develop the ITORS framework. The system was designed after reviewing current PTO recoupment practices, payroll regulations, HR technology capabilities, and compliance requirements. The framework includes five connected components: the HR Policy Engine, Absence and Balance Monitor, Consent Workflow Module, Payroll Recoupment Engine, and Compliance Reporting Hub. The compliance framework was built through a systematic review of wage deduction statutes across all 50 U.S. states, identifying three distinct regulatory patterns that the system must handle at runtime. Results: The proposed system provides an automated process for tracking negative PTO balances, collecting employee consent, calculating payroll deductions based on state-specific rules, and generating compliance reports. The framework addresses common problems found in manual PTO recoupment processes, including missing consent records, non-compliant deductions, delayed recovery actions, and limited reporting visibility. ITORS can also integrate with major ERP and HCM platforms such as Workday, Oracle HCM Cloud, SAP SuccessFactors, ADP Workforce Now, and other enterprise systems. Conclusions: ITORS provides a practical and scalable approach for managing PTO recoupment in organizations operating across multiple states. By combining compliance automation, payroll integration, employee consent management, and reporting capabilities, the framework helps organizations reduce risk, improve compliance, recover overpaid PTO more effectively, and maintain transparency with employees.
An Empirical Analysis of Reliability Challenges in Multi-Step Agentic AI Systems
The application of Agentic Artificial Intelligence (AI) systems attracts a lot of interest to undertake complex tasks through planning, reasoning, memory usage, tool interaction, and validation processes. It is common knowledge that while the applications are widely used in various fields like software engineering, healthcare, and business automation, reliability issues are still one of the primary challenges. The aim of this study is to highlight possible reliability issues in the multi-step agentic AI system, employing an experimental framework. A quantitative research design is used in which the AgentBench benchmark dataset with 5,000 task instances and 12 variables related to the workflow is used. The data pre-processing, reliability modelling and performance evaluation are performed using Python libraries and agent simulation frameworks. Task success rate, failure rate, time to execute task, task recovery rate, consistency of output and error propagation rate are used as measures of reliability. Results indicated that the reliability reduces as difficulty of the task escalates and reasoning and tool related errors are the leading causes of failure. In addition, it is found that the propagation of error over extended workflows could drastically escalate resulting in domino effects. It extends an established reliability evaluation framework, and offers ideas for developing more robust, transparent, and dependable autonomous systems that incorporate agentic AI in complex settings.
Reliability-Based Design Optimization of Hip Prosthesis Jump Distance: Monte Carlo Analysis of 1,617 Design Configurations
This study presents a reliability-based design optimization (RBDO) framework for improving the intrinsic stability of total hip arthroplasty through jump-distance-based design selection. The problem was formulated using four design variables: femoral head diameter, cup abduction angle, cup anteversion angle, and femoral offset. Jump distance (JD) was computed from an analytical geometric model, and uncertainty was propagated by Monte Carlo simulation with 10,000 samples per scenario. A total of 1,617 configurations were evaluated in MATLAB R2020b using parallel computing. The results showed a mean reliability index of 2.3945, with values ranging from -2.9677 to 3.8906, and a mean Monte Carlo jump distance of 12.0659 mm. The best-performing configuration within the explored design space was a 36 mm head, 30° abduction, 30° anteversion, and -2 mm offset, yielding a nominal JD of 20.601 mm, a mean simulated JD of 20.568 mm, and zero failures in 10,000 trials. Parametric analysis showed that abduction angle had the strongest negative influence on reliability, followed by femoral offset, whereas head diameter had a positive effect and anteversion had a weaker but favorable contribution. These results confirm that a probabilistic design framework can discriminate robust from fragile implant configurations more effectively than a purely deterministic threshold check and provide a computationally efficient basis for preclinical implant-orientation optimization.
From Pixels to Parameters: How Gpu Parallelism Defined Modern Ai
Introduction: The rapid ascent of artificial intelligence (AI) over the past decade has fundamentally transformed the landscape of computational infrastructure. At the heart of this revolution lies the Graphics Processing Unit (GPU), a technology that has transitioned from a specialized tool for geometric rendering to the primary engine driving deep learning and large-scale model training. This paper explores the architectural evolution of the GPU, tracing its lineage from fixed-function graphics pipelines to the massively parallel, throughput-oriented architectures of the modern era. We examine the fundamental shift from serial processing, characterized by the latency-optimized CPU, to the parallel paradigm of the GPU, which utilizes thousands of arithmetic logic units (ALUs) and dedicated hardware accelerators such as Tensor Cores to execute complex matrix operations. Furthermore, this study provides a comparative analysis of contemporary GPU offerings from NVIDIA, AMD, and Intel, contextualizing their role within the current AI hardware ecosystem. By evaluating the co-evolution of GPU microarchitecture and deep learning algorithms, this paper argues that the GPU is not merely a component of AI advancement but the primary catalyst for the current paradigm of generative and predictive intelligence.
Comparative Evaluation of Recent Metaheuristic Optimizers for LQR Weight Selection in Coupled Twin Rotor MIMO Systems
Choosing the right weighting matrices for a Linear Quadratic Regulator (LQR) is one of those problems that looks simple on paper but quickly becomes a challenge in practice, especially when the system being controlled is as complex as the Twin Rotor MIMO System (TRMS). The TRMS is a laboratory benchmark that behaves much like a helicopter: two rotors, strong aerodynamic coupling between pitch and yaw, and nonlinear dynamics that make any classical tuning method fall short. In this work, we take a close, fair, and reproducible look at six nature-inspired optimization algorithms published between 2021 and 2024 RIME, the Dung Beetle Optimizer (DBO), Parrot Optimizer (PO), Artificial Rabbits Optimization (ARO), the Weighted Mean of Vectors algorithm (INFO), and the Gorilla Troops Optimizer (GTO) and compare them against the Dragonfly Algorithm (DA) as a well-established baseline. All seven algorithms are used to automatically search for the best Q and R matrices that minimize a composite performance index combining ITAE and ISE criteria over closed-loop simulations. We evaluate each optimized controller on six practical metrics: rise time and settling time for both the pitch and yaw channels, percentage overshoot on each axis, and computation time. Our results show that the newer algorithms especially RIME, DBO, and PO consistently produce better controllers than DA, with RIME offering the best overall trade-off between solution quality and computational efficiency. Step-response curves, convergence plots, bar charts, and a radar chart are all provided to make the comparison as transparent as possible. This study gives engineers and researchers a practical, grounded guide for picking the right optimization tool when designing LQR controllers for coupled aeromechanical MIMO systems.
Latency-Constrained Robust Federated Learning for 6G V2X Edge-to-Cloud Networks
The integration of Federated Learning (FL) within 6G-enabled Vehicular Ad Hoc Networks (VANETs) holds significant potential for decentralized, privacy-preserving intrusion detection. However, providing Ultra-Reliable Low-Latency Communication (URLLC) while simultaneously providing defense against adversarial poisoning is a major challenge. In this paper, we propose a new framework, namely UA-RFA, based on Uncertainty-Aware Robust Federated Aggregator, which is specifically designed for the 6G edge-to-cloud continuum. As shown by our experimental evaluation, our proposed framework meets stringent Ultra-Reliable Low-Latency Communication requirements, as all vehicular updates are completed within a 20 ms window, with a mean of 13.4 ms. Although our architecture shows significant stability and robustness against adversarial updates, our experiment shows a significant phenomenon of a ‘majority class plateau’ for highly imbalanced network intrusion detection system (NIDS) datasets, resulting in a stable global accuracy of 87.3%.
Enhanced Thermal Performance of a Hybrid Solar Air Distiller through Porous Baffle Integration
In the context of growing global energy demand and increasing freshwater scarcity, the development of sustainable desalination technologies has become a major scientific and environmental challenge. Owing to its favorable geographical location, Algeria possesses considerable solar energy potential that remains largely underexploited. Among renewable energy applications, solar thermal distillation coupled with solar air heating systems represents an efficient and environmentally friendly solution for freshwater production, particularly in Saharan, arid, and semi-arid regions.This study presents the design and numerical modeling of a hybrid solar air distiller integrated with porous baffles to enhance thermo-hydraulic performance and heat transfer characteristics. The proposed system consists of a greenhouse-effect solar distiller combined with a flat-plate solar air collector. Porous baffles are incorporated within the airflow channel to improve turbulence intensity, increase air residence time, and promote convective heat exchange inside the distillation chamber. The operating principle is based on the greenhouse effect, where solar radiation is absorbed and converted into thermal energy. The heated air generated by the integrated solar collector is recovered and circulated through the distillation unit, leading to improved evaporation and condensation processes. The integration of porous baffles significantly enhances thermal energy utilization and contributes to higher system efficiency.The obtained results demonstrate that the proposed hybrid configuration improves heat transfer mechanisms and thermal performance compared with conventional solar distillation systems. Consequently, the developed system offers a promising renewable-energy-based solution for water desalination and solar heating applications in remote and water-scarce regions.
A Combined Stochastic Approach for Enhancing Power Supply Reliability and Availability of Critical Users, Using Petri Nets Model, Monte Carlo Simulation and Fuzzy Logic
Power supply interruptions and outages constitute a highly significant concern for industrial and critical users, especially in the oil and gas sector, where any interruption means a substantial economic impact. This study aims to assess the reliability and the availability of power supply within an oil and gas company located in southern Algeria, which is a region known by its harsh and severe environmental conditions such as high temperatures, strong winds, and geographical constraints. This work focused on evaluating these constraints and their impact on energy availability using dynamic reliability approach: Petri nets model PNM and Monte Carlo simulation MCS coupled with fuzzy logic to assess dynamic variables. Three different power networks were evaluated, and the results indicated that environmental constraints have a significant impact on power supply availability resulting from the occurrence of common cause failures, delays time, and the probability of failure on demand. These results emphasize the necessity of dynamic evaluation of power networks rather than relying solely on static data, which may lead to incorrect decisions regarding the choice of backup energy sources.
A Detail survey on Pedestrian Trajectory Prediction in Smart Cities for Real-Time Surveillance
Pedestrian trajectory prediction has become a foundational capability for intelligent systems operational within dynamic human environments, such as autonomous vehicles, service robots, and smart surveillance platforms. This paper systematically examines the recent advances in pedestrian trajectory prediction, with a particular focus on spatiotemporal deep learning architectures designed for real-time and edge-based deployment. While temporal dependencies have been modelled using recurrent and probabilistic approaches, these methods often struggle with the computational inefficiency and the limited spatial context modeling. In contrast, transformer-based networks and 3D convolutional neural networks (3D CNNs) provide richer spatiotemporal representations but face challenges in scalability and deployment on embedded systems. The reviewed literature is categorized into primary methodological frameworks, recurrent, convolutional, hybrid, and transformer-based models. The frameworks have achieved major advancements in three areas which include social interaction modeling, multimodal prediction, and contextual scene understanding. The evaluation of models using standard benchmarks such as ETH/UCY and the Stanford Drone Dataset shows that prediction accuracy and inference latency and model generalization have important trade-offs. The field faces ongoing obstacles which include difficulties with extended future predictions and challenges in handling uncommon behaviors and the need for real-world performance. The research paper provides a roadmap which will help develop lightweight 3D CNN architectures that use temporal residual connections to support efficient spatiotemporal reasoning on edge devices with limited resources.
Experimental Studies on Compressive Behaviour of Granite Using Quasi-Static and Split Hopkinson Pressure-Bar Test.
The mechanical characteristics of the materials will change because of the shift in strain rates. This study employs several compressive experiments to investigate the static and dynamic behaviour of the granite specimen at various strain rates. The results of the experiment showed that the granite's mechanical properties and fracture behaviour had changed dramatically. However, the findings of compressive behaviour in the SHPB test revealed more micro-cracks as the strain rates increased. Additionally, the phase pattern of a granite specimen captured using a scanning electron microscope (SEM) shows intergranular fractures, and the grain-scale river pattern is observed in the data.
Secure Event-Driven Microservices in Regulated and Hybrid Environments: Architecture, Governance, and Compliance
But in regulated sectors, the question becomes how to preserve the rapid time to market and flexibility of the distributed microservices architectural style while satisfying the deterministic, end-to-end, and thorough regulatory compliance controls that financial, health, public sector, and other regulators have depended on: centralized audit tables, centralized transaction boundaries, and centralized gateways in legacy monolithic and mainframe-based architectures. The event-driven microservices architectural style provides the solution, eliminating the need for a centralized gateway and implementing auditability and governance throughout all independent components through asynchronous message flows. This new architectural style can only succeed if compliance is not just a bolt-on afterthought but built into every level of the distributed system. Zero Trust identity propagation in event metadata, policy-as-code for event processing in continuous deployment pipelines, architectural styles for idempotent and auditable event processing, and structured threat models mapping categories of threats to verifiable mitigations above and below the architectural level form a basis for interoperable compliance architecture. Likewise, delivery guarantees, strategies for evolving event schemas, domain-driven event stream decomposition, and operational resilience patterns, which all have direct regulatory consequences, need to be encoded at the architecture layer rather than be treated merely as eventual compliance gates after deployment. The domain of compliance-as-code and federated governance is likewise characterized by evidence being produced through automated system behavior rather than retrospective documentation artifacts.
Strategic Analysis of the Reliance-Disney Joint Venture Formation of Jio Star and India's Media Consolidation: A Case Study in Joint Venture Strategy, Platform Economics, and Media Market Consolidation
This paper aims to provide a strategic, financial, and regulatory analysis of the merger between Reliance Industries Limited (RIL) and Disney's media assets in India, resulting in the creation of JioStar, the biggest media and entertainment business in India. Initiated on February 28, 2024, and closed on November 14, 2024, the deal involved a post-money valuation of USD 8.5 billion for the newly formed joint venture company. This paper analyzes the pre-merger environment that forced both RIL and Disney to come to such a solution: operational losses of Disney, loss of customers for Disney+Hotstar due to loss of IPL digital broadcasting rights, and emergence of Reliance's Viacom18 as a competing player in the market. This research looks into the structure of the deal and ownership distribution (Reliance/Viacom18: 63.16%, Disney: 36.84%), its rationale, and drivers of synergy by applying scenario-based enterprise valuations (based on three scenarios of IPL rights renewing in 2027), calculating HHI, applying game theory perspective, and assessing Porter's Five Forces. SWOT analysis is included in Appendix A as an additional material. From the post-merger performance figures, the rate at which JioStar monetized its business model is quite impressive. The firm recorded one hundred million paid subscribers on the OTT platform within five weeks of its launch. It also captured thirty-four percent market share in linear TV viewing and had an EBITDA of USD 196 million with a margin of 24.0% in the second quarter of 2026 financial year. From the scenario valuation, a spread of USD 21.6 billion exists between the two extreme points - all attributed to the rights acquisition for the Indian Premier League in 2027. This paper, therefore, shows that this merger is quite a unique multi-faceted case study.
An Investigation into the Extent to Which AI-driven Automation Altered the Structural Economic Viability of Dropshipping as an Entrepreneurial Model.
This study examines the extent to which AI-driven automation has altered the structural economic viability of dropshipping as an entrepreneurial model. While dropshipping has traditionally been characterized by low barriers to entry and minimal capital requirements, it has also faced significant structural limitations, including thin profit margins, high competition, and reliance on third-party suppliers. Using a qualitative research approach based on secondary data and conceptual economic analysis, this paper evaluates the impact of AI across three key dimensions: skill barriers, capital efficiency, and market competition. The findings indicate that AI significantly reduces the skill and knowledge required to operate a dropshipping business while improving capital efficiency through automation of marketing, product research, and content creation. However, these same developments contribute to increased market saturation, intensifying competition and raising customer acquisition costs through auction-based advertising systems. As a result, while AI enhances accessibility and short-term operational efficiency, it does not fundamentally resolve the structural constraints of the model. The study further identifies an emerging shift toward “branded dropshipping” as an adaptive response enabled by AI, allowing for greater differentiation and potential margin improvement. Nevertheless, this evolution does not eliminate underlying economic limitations. Overall, the research concludes that AI has not made dropshipping inherently more sustainable but has instead reshaped its dynamics, creating a more accessible yet increasingly competitive and uncertain entrepreneurial environment.
Corporate Governance Mechanisms and Prevention of Financial Fraud: A Review of Indian Corporate Scandals
Corporate governance serves as the foundational pillar for ensuring transparency, accountability and ethical conduct in modern corporations. India's corporate landscape has been repeatedly scarred by high-profile financial frauds from the Harshad Mehta securities scam of 1992 to the Satyam Computer Services scandal of 2009, the Punjab National Bank (PNB)-Nirav Modi fraud of 2018, the Infrastructure Leasing and Financial Services (IL&FS) collapse of 2018 and the Yes Bank crisis of 2020 exposing persistent deficiencies in governance mechanisms, regulatory oversight and auditor independence. This review article critically examines corporate governance mechanisms including board composition, audit committee effectiveness, ownership structures, whistleblower policies and regulatory frameworks and evaluates their role in preventing financial fraud in the Indian corporate context. Drawing on agency theory, stewardship theory and the fraud triangle model, the review synthesises evidence from landmark Indian corporate scandals, statutory provisions under the Companies Act 2013, SEBI's Listing Obligations and Disclosure Requirements (LODR) Regulations 2015 and the Kotak Committee recommendations (2017). The review finds that while India's legislative architecture for fraud prevention has substantially strengthened, enforcement gaps, promoter concentration and auditor collusion continue to undermine governance effectiveness. The article concludes with policy recommendations to build a more resilient corporate governance ecosystem in India.
TaxRadar: AI-Driven Detection of Unreported Use Tax in E-Commerce
Use tax non-compliance in e-commerce constitutes one of the most persistent and under-studied forms of tax revenue leakage in modern fiscal systems. Unlike sales tax, which is collected at point-of-sale by registered merchants, use tax depends on voluntary self-assessment by individual purchasers, a mechanism that consistently produces significant under-remittance. This paper proposes, formalizes, and evaluates TaxRadar, an AI-driven detection framework for identifying unreported use tax obligations in e-commerce transaction streams. The framework integrates a seven-layer data processing and inference architecture, a probabilistic taxability classification model combining fine-tuned gradient-boosted ensembles with deterministic jurisdiction rule constraints, a graph neural network (GNN) module for entity-relationship-based risk propagation, and a composite risk scoring function with formal optimization objective. This paper presents a comparative experimental evaluation on a synthetic benchmark (UseTaxBench) against 13 baselines—spanning rule-based systems, classical ML, and state-of-the-art GNN fraud detectors—suggesting that the proposed framework can improve detection accuracy and reduce false positives relative to traditional rule-based systems on synthetic benchmark data. Privacy-preserving deployment strategies employing federated learning and differential privacy are analyzed. The framework is designed for integration with government tax authority platforms and ERP systems, enabling automated return pre-filling, audit prioritization, and taxpayer notification at scale.
Fusion-Based CNN-ViT Model for Breast Cancer Classification and Detection Using Digital Mammograms
Although it can affect men as well, breast cancer is currently the most common type of cancer that mostly affects women. It manifests when aberrant cells in breast tissue proliferate quickly and develop into tumors. Physicians use the mammogram procedure to examine the breast in order to diagnose early cancer. These mammograms fall into one of two cat- egories: benign or malignant.By merging Convolution Neural Networks (CNNs) with Vision Transformers (ViTs), this study attempts to improve classification accuracy and solve the variability and potential oversight in radiologists’ manual mammography interpretations.. ViTs capture the global context but need a lot of data and processing power, while CNN is an effective image classification system that leverages hierarchical feature extraction. In this study, we trained a CNN+ViT model using CLAHE- enhanced mammography pictures from Kaggle. For comparative study, we have also employed a few pre-trained models, including DenseNet, In- ception, SE Resnet, and XceptionNet. With an accuracy of 90.1%, the CNN+ViT model demonstrated strong performance. XceptionNet’s perfect accuracy could be a sign of overfitting
Parallel Deep Learning Framework with Feature-Level Fusion for Early Sepsis Prediction in ICU Settings: Toward Real-Time Clinical Decision-Making
Sepsis remains a major cause of ICU mortality, requiring accurate early prediction for clinical decision-making. Conventional deep learning (DL) architectures based on feature-level fusion, even when high-performing, are limited in capturing dynamic feature dependencies and may not ensure real-time applicability. We propose the Parallel Neural Network Fusion Predictor of Sepsis (PNNFPS), an ICU-oriented early prediction framework that employs two Deep Neural Networks (DNN1, DNN2) to process distinct clinical modalities in parallel, with their outputs fused and fed into an artificial neural network (ANN) to estimate sepsis risk. The approach is structured into two complementary phases: (1) offline development and optimization, using 5-fold cross-validation with class-imbalance handling, feature extraction, hyperparameter optimization, automated feature selection, and post-hoc explainability; and (2) online simulation, where the best offline model is deployed in a simulated ICU environment under four alarm policies (low, medium, high, multi-threshold) combined with an 8-hour silencing mechanism to reduce redundant alarms. The resulting alarm streams are evaluated through patient-wise detection, window-wise predictive behavior, alarm burden, alarm reduction, and lead-time analysis. Using the 2019 PhysioNet/Computing in Cardiology Challenge dataset, PNNFPS achieved a competitive offline U-score of 0.673, outperforming previously reported approaches. SHAP-based explainability confirmed clinical consistency, identifying Temperature, Heart Rate, HCO3, pH, WBC, Lactate, and Calcium as key predictive contributors. In online evaluation, the medium-threshold policy provided the most balanced operating point, preserving high patient-wise sensitivity (90.6%) with improved specificity (40.8%) and PPV (18.6%) over the low-threshold policy, an extended lead time of 89 hours before sepsis onset, and substantial alarm reduction (85.64%) via silencing. These findings indicate a favorable balance between patient safety, alarm reliability, and clinician workload, supporting PNNFPS for real-time ICU monitoring and clinical decision support.
Direct Power Control of a Three-Phase PWM Rectifier Based on Backstepping with Integral Action for Enhanced Robustness and Power Quality
Introduction: Three-phase pulse-width modulation (PWM) rectifiers require highly efficient control strategies to ensure power quality and system stability. While Direct Power Control (DPC) is widely used, standard approaches often struggle with steady-state errors, power ripples, and sensitivity to load variations. Objectives: This paper aims to design and validate an advanced DPC strategy that enhances tracking accuracy, eliminates steady-state errors, and improves the overall robustness of a three-phase PWM rectifier under diverse operating conditions. Methods: The proposed control strategy integrates a nonlinear backstepping design with an added integral action. This combination is specifically engineered to handle system nonlinearities while actively compensating for load variations. The performance and validation of the control loop were carried out through comprehensive simulations within the MATLAB/Simulink environment. Results: The simulation results demonstrate that the proposed strategy significantly minimizes active and reactive power ripples, reduces the Total Harmonic Distortion (THD) of the grid side currents, and consistently achieves a unity power factor. Furthermore, the system successfully eliminates steady-state errors under load variations. Conclusions: The backstepping DPC with integral action proves to be a highly effective solution for three-phase PWM rectifiers. It delivers a fast dynamic response, robust disturbance rejection, and superior power quality, making it a viable option for high-performance power conversion applications.
The Corporate-Led Stake-Fund Innovation as Alternative Policy Framework for the Mining Sector in the Global North and Global South: A Case Study of Four Large-Scale Mining Companies in USA and Ghana.
Corporate Social Responsibility (CSR) has been scholarly discussed from the perspectives of zero-sum (cost) and positive-sum (profitability) to business in the mining sector. The study explored stakeholders’ experiences of CSR initiatives during production and post-mining life in the host communities, CSR funds (CSRF) mechanisms, its governance and management structure, and multistakeholder co-created funds, and co-management as alternative innovation model for community led-development to the traditional CSR approach notable in the sector. The research study used a qualitative approach, without human subjects interview, based on secondary materials and case studies of four large-scale mining companies from the Global North and Global South - Resolution Copper Mining (USA), Nevada Gold Mines (USA), Newmont Ahafo Gold Mine (Ghana) and AngloGold Ashanti Obuasi Mine (Ghana) - to gain understanding of the larger social phenomenon. The study identified lack of multistakeholder co-created funds and co-management policy framework in the mining sector; mining companies are sole CSR funders of community foundations and trust funds; variability of CSR funding, governance and management mechanisms; gaps in the structured participatory governance and management mechanism partly due to vertical power dynamics obliquing transparency and accountability; government incentives and establishment of trust funds as a permitting condition drive CSR and sustainability practices; voluntary CSR approach being adopted in Ghana and USA; stakeholder value approach as business sustainability case, environmental and social harm; strong stakeholder engagements and grievance mechanism impact mining companies’ CSR and sustainability practices; and mining companies’ alignments with applicable national laws and international standards which are fragmented and loosely enforced. Recommendations were made for the industry stakeholders for considerations to address the CSRF limitations with an alternative policy model..
An enhanced rectangular Membrane Finite Element Based on the Strain Approach
This study presents the development of a enhanced rectangular membrane finite element using the strain-based approach, designed for both elastic and elastoplastic analyses. Each of the four corner nodes of the element is characterized by three degrees of freedom: two translational degrees and one in-plane rotational degree. The displacement functions of the proposed element accurately capture the rigid body motion modes. For the elastoplastic computations, the material response is evaluated using the Von Mises failure creterion, and the nonlinear solution procedure incorporates viscoplastic-strain approaches. For validation purposes, several selected numerical examples are solved using the developed element in both linear and nonlinear analyses. The results obtained are compared with analytical solutions and other similar membrane finite elements, demonstrating the strong performance of the proposed element.
FLIP-Science: A Federated Learning Framework for Privacy-Preserving Collaborative AI Research
The growing reliance on artificial intelligence in scientific research has created a demand for large-scale datasets that institutions hold under strict privacy obligations. Centralized machine learning approaches require data aggregation into a single repository, conflicting with data governance and regulatory requirements. While federated learning supports decentralized model training, existing frameworks focus primarily on algorithm development rather than providing infrastructure for collaborative research workflows. This paper presents FLIP-Science, a federated learning framework that enables privacy-preserving collaborative AI research across multiple institutional nodes. The system introduces an adaptive trust-weighted aggregation mechanism that incorporates client reliability, dataset quality, and communication efficiency to improve model convergence under heterogeneous conditions. Experimental evaluation on standard benchmark datasets demonstrates that FLIP-Science achieves higher accuracy and faster convergence than conventional federated baselines, while maintaining scalable and responsive infrastructure performance suitable for real-world multi-institution deployments.