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
Assessing Readiness for AI and Blockchain Integration in Indonesia's Coconut Ecosystem: A Multi-Stakeholder Analysis
Introduction: Indonesia, a leading coconut producer, faces supply chain inefficiencies, traceability challenges, and limited value-added processing. While AI and blockchain offer solutions, successful adoption requires a comprehensive readiness assessment. Objectives: This study evaluates Indonesia's coconut ecosystem readiness across six dimensions: knowledge, infrastructure, human capital, regulatory frameworks, financial resources, and process maturity. Methods: A qualitative survey of 18 stakeholders (government officials, processors, collectors, suppliers, farmers, breeders, and associations) was conducted from June to October 2025 using thematic analysis. Results: Results reveal a "readiness paradox": high adoption enthusiasm despite significant capability gaps. The Composite Technology Readiness Index scored 2.3/10 (pre-readiness stage), with over 80% possessing minimal AI/blockchain knowledge. Critical gaps include regulatory frameworks (10% readiness), human capital (20%), knowledge (20%), and process maturity (20%), while infrastructure showed relatively better readiness (30%). Conclusions:Key pain points price volatility, transparency deficits, and traceability inadequacies, align well with technology capabilities. Universal concern for farmer welfare provides a strong foundation for multi-stakeholder collaboration.
A Dynamic and Adaptive Decision Support System for Managerial Time Allocation Under Environmental Uncertainty
Introduction: Managerial time allocation is a critical mechanism through which managers influence organizational performance and adaptability. Existing decision support approaches typically treat time allocation as a static, one-shot optimization problem, despite the fact that managerial priorities, constraints, and environmental conditions evolve continuously. In uncertain and dynamic environments, static recommendations risk becoming misaligned with organizational needs, reducing their practical usefulness. Objectives: The objective of this study is to develop and evaluate a dynamic and adaptive decision support framework for managerial time allocation that explicitly accounts for environmental uncertainty and temporal change. The study aims to assess whether adaptive time-allocation policies outperform static and purely reactive approaches in terms of cumulative value, robustness, and behavioral stability. Methods: Managerial activities are modeled as a time-dependent portfolio, with time allocations revised periodically using a rolling-horizon decision process. Managerial preferences are represented through interval-valued parameters to preserve flexibility, while environmental uncertainty is captured via stochastic states and scenario-based evaluation. The resulting optimization problem is solved using a robustness-aware rolling-horizon evolutionary algorithm. Performance is evaluated through simulation under multiple uncertainty regimes, including low volatility, high volatility, shock-driven, and regime-switching environments, and compared against static and non-robust rolling-horizon benchmarks. Results: The results show that dynamic adaptation provides limited benefits in stable environments but yields substantial performance improvements under volatility, shocks, and regime changes. The proposed adaptive robust model consistently achieves higher cumulative value and lower performance variability than benchmark approaches. Dynamic analysis further reveals that the model selectively adapts to meaningful environmental changes while avoiding excessive oscillations in time allocations. Conclusions: The findings demonstrate that managerial time allocation should be supported by adaptive, uncertainty-aware decision support systems rather than static schedules or purely reactive adjustments. By formalizing time allocation as a dynamic portfolio problem, this work contributes to the decision support and management science literature and provides a foundation for intelligent systems that evolve alongside managerial environments.
BrainFusion: Deep Learning for Early Detection and Grading of Brain Tumors Using Medical Imaging and Clinical Data
Brain tumors are still one of the most dangerous and deadly types of cancer. Early detection and correct grading are very important for effective treatment. This study presents a dual-stage artificial intelligence framework that utilizes advanced transformer-based deep learning architectures, specifically the Swin Transformer and Vision Transformer (ViT), for the early detection and grading of brain tumors through MRI images and clinical metadata. Our approach takes advantage of these transformer models' better feature extraction and contextual representation, which is not the case with traditional CNN-based methods. The framework consists of two core components: (1) a transformer-based classifier for detecting tumor presence in 2D MRI slices, and (2) a multimodal fusion model combining imaging features and clinical data (age, gender, tumor type, grade) to predict tumor grade (HGG vs LGG). We also made a web interface powered by Gradio that lets users upload images and get a diagnosis in real time. This makes the model available to both researchers and doctors. We use publicly available brain MRI datasets like BraTS and TCGA to create and test our model. show that the Swin Transformer is better than current CNN-based methods at finding tumors and classifying grades, with an accuracy of up to 98.3% and 95.2%, respectively. These findings validate the effectiveness and promise of transformer architectures and web-based AI tools in enhancing neuro-oncological diagnosis.
Technology Acceptance and Consumer Behavior: A Study of Online Shopping in Indonesia
Introduction: The growth of this information technology allows every individual to easily involve themselves in legal activities, such as buying and selling transactions. The rapid development of the internet has had a significant impact on various aspects of our lives, facilitating interaction, communication, and trade efficiently and economically, even involving people from all over the world. By using TAM, buying and selling goods is getting easier. Objectives: This study aims to determine the effect of ease of use, trust, web quality, and perceived usefulness on consumer purchasing decisions in the TikTok online shop on Generation Z in Central Java, Indonesia. Methods: This study adopts a descriptive quantitative method, designed to illustrate the qualities (features) of a scenario through an impartial lens. This incorporates gathering and scrutinizing numerical information, alongside the application of statistical assessments. The analysis technique used in this study is Partial Least Squares (PLS), which is processed using Smart PLS software version 4.0. The analysis includes descriptive statistical analysis, outer model testing, inner model testing, and hypothesis testing. Results: For Business Owners/Sellers on TikTok Shop: Trust significantly influences purchasing decisions. Therefore, sellers need to build consumer trust through clear product information, a transparent returns system, and maintaining a reputation through positive customer reviews. For TikTok Platform Managers: Continuous improvements to the user interface (UI) and user experience (UX) are needed to ensure consumers feel comfortable shopping, even if ease of use is no longer a dominant factor. For Consumers: The findings of this study can serve as a basis for consumers to be more selective in choosing online sellers, by considering aspects of platform trustworthiness and quality rather than just ease of use. Consumers are also encouraged to utilize security features and read reviews from other buyers before making a transaction.For Academics and Researchers: This study emphasizes the importance of trust and web quality factors in the online shopping behavior of Generation Z. These implications can be used as a reference for developing digital marketing models that are more focused on building consumer loyalty and a sense of security. Conclusions: This study applies the Technology Acceptance Model (TAM) to analyze the behavior of Generation Z consumers in Indonesia using TikTok Shop, which is still rarely studied in the context of the Indonesian local market. From an academic perspective, this research expands the literature on the application of TAM to online shopping behavior by adding trust and web quality variables. Practically, it provides insights for businesses to prioritize trust and web quality in their e-commerce strategies.
Women’s Empowerment in the IT Sector under Remote Work: The Role of Organizational Commitment, Job Loyalty, and Work–Life Balance Policies
Introduction: Work from home gets the whole world flocked into their home. Everyone has to do their own work confine at home. The work of women increased by manifold. In this situation it was of great interest to know how the working women balances her work and life and whether their job loyalty (JL) and organizational commitment (OC) is influenced by the companies work life balance policies (WLBP). Objectives: The primary objective of this study is to examine how organizational work–life balance policies influence organizational commitment and job loyalty among working women in the software industry during the work-from-home period. Methods: This study is carried out in 3 software firms of India, by using primary data collected online from women respondents through a structured questionnaire. Data are analysed by correlation and regression method. Results: The study explores that there is a significant positive correlation between WLBP with OC and JL. On the basis of the results the study proposes a conceptual model on WLBP, OC and JL. Conclusions: The study has significant practical implication in organisational policy formulation, human resource management and organisational effectiveness.
The Role of Artificial Intelligence in HRM–Marketing Integration: A PLS-SEM–Informed Review
AI is revolutionizing HRM and marketing through data-driven decision-making, predictive analytics, and automated workflows. Academic research on AI-driven HRM–Marketing integration has grown as companies adopt AI-enabled employee and customer strategies. Partial Least Squares Structural Equation Modelling (PLS-SEM) is the leading analytical method for modelling complex, multidimensional, and often formative phenomena, including AI capability, people analytics, customer analytics, and cross-functional performance. However, a systematic evaluation of PLS-SEM in this intersection is absent. This systematic literature review synthesizes 97 peer-reviewed studies (2015–2025) from Scopus and Web of Science using PRISMA 2020. The review highlights internal–external experience alignment, cross-domain predictive analytics, and algorithmic coordination as AI-enabled integration pathways. While PLS-SEM is well-suited for capturing these relationships, findings reveal methodological inconsistencies, such as weak justification, limited algorithm configuration reporting, insufficient predictive assessment, and underuse of advanced techniques (e.g., higher-order constructs, PLSpredict, FIMIX-PLS, endogeneity controls). This paper demonstrates how AI promotes cross-functional HRM–Marketing alignment and offers best practices to improve the rigour, transparency, and predictive value of future PLS-SEM–based research.
Artificial Protozoa Optimizer for Enhanced Robust and Secure Image Watermarking
Digital image watermarking faces significant challenges in balancing imperceptibility and robustness under diverse distortions. With the growing prevalence of deepfake and image manipulation technologies, preserving the authenticity and integrity of digital images has become increasingly critical. This paper presents a novel hybrid optimization-based framework for digital image watermarking, integrating the Arnold Transform (AT), Discrete Wavelet Transform (DWT), Singular Value Decomposition (SVD), and the Artificial Protozoa Optimizer (APO). The watermark, such as a QR code, is first encrypted using the Arnold Transform and embedded into the low-frequency sub-band via SVD, while the APO determines the optimal scaling factor α through an objective function combining the Structural Similarity Index Measure (SSIM) and Normalized Cross-Correlation (NCC) ensuring an optimal balance between imperceptibility and robustness. Experiments on a diverse set of images, including medical (e.g., MRI and chest X-ray) and standard benchmark images (e.g., Baboon, Peppers), demonstrate that the proposed framework achieves high SSIM and NCC, along with low Learned Perceptual Image Patch Similarity (LPIPS) and Bit Error Rate (BER). These metrics ensure accurate watermark extraction under attacks such as JPEG compression, noise, and rotation, while supporting ownership protection and multimedia authentication.
Automating Healthcare Authorization Letters Through Intelligent Systems
Healthcare organizations face significant challenges in managing authorization correspondence, requiring precise coordination across multiple system components while meeting stringent regulatory requirements and ensuring accessibility for diverse patient populations. Traditional manual processes for generating approval and denial letters create substantial administrative burdens, with physicians dedicating nearly two full business days weekly to prior authorization activities, leading to workflow bottlenecks and inconsistent communication quality. Modern healthcare platforms address these challenges through sophisticated automation frameworks that integrate artificial intelligence technologies to streamline correspondence workflows while maintaining regulatory compliance. The implementation of service-oriented architectures built on health informatics standards enables seamless interoperability between authorization management systems and correspondence engines, facilitating automated extraction and population of member letters with complete accuracy. For denial letters specifically, generative artificial intelligence transforms complex clinical rationale into member-accessible language targeting sixth-grade reading comprehension levels, addressing critical health literacy barriers that impede patient understanding of coverage decisions. Natural language processing technologies analyze clinical documentation and member case histories to generate personalized, contextually relevant explanations that synthesize information across the entire authorization lifecycle. Responsible implementation incorporates human-in-the-loop architectures where qualified supervisors review AI-generated content for accuracy, appropriateness, and cultural sensitivity before final distribution. This hybrid model optimizes resource allocation by automating routine translations while reserving human expertise for complex scenarios requiring professional judgment. The integration of intelligent automation with bidirectional communication patterns creates closed-loop systems that track correspondence from generation through postal delivery, establishing comprehensive audit trails necessary for regulatory reporting and quality assurance initiatives essential for value-based care arrangements.
Strategic Go-to-Market Models in Enterprise Software: Free, Trial, and Proof-of-Concept Approaches
Customer acquisition strategic frameworks through corporate software spending have gradually changed, and nowadays the enterprises decide among one of the three: freemium models, time-limited trials, and proof-of-concept implementations based on the sophistication of the product, the characteristics of the target audience, and the limitations of the operations. Every roll-out plan is functioning under a different control system of restrictions - in the case of a freemium product, the limitations are related to the features or the capacity of the product; the temporal boundaries define the duration of the implementation of the trial; and the scope of the validation criteria for enterprises is what defines a proof-of-concept engagement. Digital transformation agendas have, on the one hand, changed the conventional software delivery methods and, on the other hand, have opened the way for self-service adoption mechanisms and have eliminated the friction points that usually hinder market penetration. The emergence of artificial intelligence-integrated software introduces fundamental shifts in deployment economics, particularly regarding computational resource allocation and cost recovery mechanisms. Traditional customer acquisition paradigms require substantial modification when inference costs and token-based pricing models become primary economic considerations. Transformer architectures process sequences through attention mechanisms that scale computational requirements with context length, creating direct relationships between usage patterns and operational expenses. Freemium models remain viable for AI applications, but demand careful capacity management through token allocation limits and query frequency restrictions. Proof-of-concept engagements experience the most substantial impact due to extended validation durations and intensive computational workload patterns characteristic of enterprise requirements, driving preference for paid validation structures with explicit cost recovery mechanisms and contractual commitment frameworks. In AI-native enterprise software, every user interaction burns GPU cycles and can execute actions in external systems. This changes how thinking about Free, Trial, and POC motions should proceed. In AI-native enterprise software, go-to-market strategy is no longer just a question of friction and conversion—it is also a question of inference cost, safety, and trust. Each user interaction consumes non-trivial GPU resources and, in the case of AI agents, may trigger actions across external tools and data sources. This article compares freemium models, time-limited trials, and proof-of-concept engagements as GTM entry motions for AI-enhanced enterprise products, with particular emphasis on inference economics, token-based pricing, and safety validation during customer evaluation.
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Optimizing Full-Stack Application Performance through End-to-End Observability and Real-Time Diagnostics
In full-stack applications, performance problems are often not limited to the frontend or backend but can affect the entire stack as distributed architectures become increasingly common. When an issue arises in one component, it may quickly cascade throughout the application stack, leading to performance degradation. Customary application monitoring tools tend to provide visibility into different system layers in isolation. Because distributed systems can involve many interconnected components, end-to-end observability seeks to overcome these shortcomings by providing a single view of the application throughout the entire application stack. Telemetry is typically divided into three groups: logging, metrics, and distributed tracing, which provide different views of system behavior. Standardized telemetry collection allows interoperability between heterogeneous technology stacks, and effective instrumentation is the foundation of observability. Frontend instrumentation provides real user monitoring, whereas backend instrumentation provides server-side metrics, such as request latency distribution and the percentage of requests that experience failures. By relating user-reported latency to the backend processing time, targeted optimizations can be performed. Observability platforms use machine learning algorithms to uncover patterns that are overlooked by threshold-based alerts. Large language models are promising candidates for automating root cause analysis in complex cloud systems.
Examining the Economic and Business Impact of Cloud-Based Disaster Recovery
Cloud-based disaster recovery has emerged as a transformative alternative to conventional infrastructure protection strategies. Traditional disaster recovery models demanded substantial capital investments in redundant physical facilities. Secondary data centers remained idle during normal operations while consuming significant resources. The shift toward cloud-native recovery architectures addresses fundamental inefficiencies inherent in legacy protection frameworks. Consumption-based pricing models eliminate upfront hardware procurement requirements. Operational expenses replace capital expenditures through pay-per-use service arrangements. Site reliability engineering practices allow for automation in failure detection and response. Structures that can be allotted geographically throughout different availability zones can resist a regional disruption. Non-stop replication of statistics enables in reduction of the threat of information loss for the duration of a failover situation. Resource provisioning that is elastic can be compared to a dynamic capacity which is changing along with the business requirements. Scaling operations proceed without procurement delays or fixed capacity constraints. Resource scheduling algorithms optimize workload distribution across available infrastructure. Investment evaluation requires consideration of direct cost savings alongside risk mitigation value and strategic benefits. Decision support frameworks assist organizations in navigating adoption complexity through structured assessment mechanisms. The economic advantages of cloud-based disaster recovery extend beyond simple cost reduction to encompass improved organizational agility and enhanced competitive positioning.
Resource Optimization and Cost Management in Kubernetes Clusters: A Framework for Container Orchestration Platforms
The use of container orchestration platforms has changed the application deployment patterns, but cost management has remained an operational issue. Kubernetes environments are often resource inefficient due to over-provisioning, node fragmentation, workload imbalance, too much storage allocation, and high network transfer costs. This framework introduces systematic approaches to cost optimization in Kubernetes clusters, leveraging built-in visibility strategies, configuration tuning, and workload-based optimization strategies. The proposed approach integrates a cluster telemetry system and allocation tracking to create end-to-end cost visibility for containerized infrastructure. This optimization of resources can be achieved by right-sizing using utilization signals, vertical scaling policy, horizontal scaling policy, and bin-packing efficiency-enhancing techniques that minimize idle capacity by using dynamic provisioning policies. Mixed-instance node fleet deployment, the adoption of spot instances with disruption-aware scheduling, and long-term capacity planning based on savings plans are among the compute cost reduction methods. These strategies and practical tips help companies using Kubernetes to consistently save on infrastructure costs while ensuring that their workloads remain reliable, perform well, and meet compliance requirements during production.
Modern Data Platform Evolution: From Legacy Warehouses to Cloud-Native Ecosystems
The transition from monolithic, infrastructure-bound data platforms to modern, cloud-native designs signals a model shift toward higher levels of scalability, flexibility, and enterprise trust. Conventional, on-premises data warehouse techniques rely on tightly coupled architectures and batch processing, often leading to operational and performance challenges. Distributed computing frameworks enabled horizontal scaling and polyglot persistence patterns but added important complexity to the ecosystem. Cloud-native architectures improved the data platform by separating storage and computing, allowing for flexible resource use and the availability of managed services to reduce operational burdens. The lakehouse architecture brings together the advantages of data lakes, like flexibility, and the benefits of data warehouses, such as ACID transactions, schema enforcement, and detailed access control, while also allowing teams to manage their own data areas in line with company rules through federated data governance. Modern architectures use new technologies like embedded AI, streaming, and metadata-driven pipeline creation to help organizations build reliable, scalable, and affordable data systems that speed up decision-making, support various advanced analytics, and align infrastructure costs with business results across the distributed enterprise.
The Autonomous Financial Advisor: A Framework for AI-Powered Personal Finance Management
The article provides an overview of the framework to create an AI-based Personal Finance Advisor (AIPFA) that would democratize access to advanced financial advice. Conservative financial advice has long been limited to people with a great deal of wealth, and the available digital financial advice systems are mostly descriptive as opposed to prescriptive. The AIPFA system proposed changes to this paradigm by a multi-layered architecture, which involves the aggregation of data, an advanced AI engine based on a four-stage analytical maturity model, and personalization features in addition to a conversational interface. The key aspect in this framework is a well-developed ethical governance model with fairness, transparency, and user-centric optimization. The article describes every element of the architecture, discusses how descriptive analytics transforms into prescriptive analytics, sets ethical standards in autonomous financial advice, and provides a roadmap for gradual implementation. Through this framework, financial institutions will be able to provide personalized and proactive financial advice at scale and enhance customer relationships and close key financial wellness gaps among underserved people.
Expanding Ad Inventory Through New Surface Monetization: A Technical Analysis of Discovery Surface and Creator Content Surface Implementation
This article examines the technical implementation of advertisement integration into previously unmonetized surfaces within a major social media platform ecosystem. It details the engineering approaches, system modifications, and performance optimization techniques employed to monetize these surfaces while maintaining positive user engagement metrics successfully. The article investigates how the engineering team extended the advertisement rendering system to accommodate diverse creative formats tailored to unique display requirements, implemented backend eligibility framework extensions for surface-specific rule implementation, and established comprehensive real-time monitoring capabilities. Additionally, the article explores the sophisticated revenue attribution mechanisms developed for content creator monetization, including creator revenue sharing and eligibility determination algorithms. Through cross-functional technical integration across finance systems, organic surface protection mechanisms, and infrastructure scaling solutions, the implementation achieved successful monetization expansion without compromising user experience quality, demonstrating how careful technical planning, cross-functional collaboration, and data-driven decision-making can balance revenue objectives with platform integrity.
Identity and Access Management (IAM) Expertise and Organizational Success Stories
Identity and Access Management (IAM) has evolved into a strategic security discipline that manages digital identities and controls resource access across complex enterprise environments. This article examines core IAM components including identity lifecycle management, access governance frameworks, authentication mechanisms, and Privileged Access Management (PAM) systems that provide comprehensive security capabilities. Implementation success requires orchestrated organizational strategies addressing technical complexities, stakeholder engagement, and change management while establishing measurable metrics across security, compliance, and operational dimensions. PAM emerges as a critical component requiring enhanced security controls and behavioral analytics to protect high-value accounts. Regulatory frameworks including HIPAA, SOX, and GDPR significantly influence IAM design and operational procedures through sophisticated audit processes. Emerging technologies such as AI-driven behavioral analytics, cloud-native architectures, and zero trust models reshape IAM capabilities while creating challenges in hybrid cloud environments, IoT proliferation, and API security. Healthcare case study analysis demonstrates that successful implementations require executive support, phased approaches, and alignment with digital transformation initiatives. Organizations treating IAM as a strategic enabler rather than compliance obligation achieve greater value realization and competitive advantages. Future IAM success depends on continuous adaptation to technological evolution, threat landscape changes, and regulatory developments while maintaining focus on business objectives and user productivity requirements.
Synergizing Human Expertise and Enterprise Architecture: Collaborative Innovations in Healthcare, Manufacturing, and Scientific Research
The unprecedented technological complexity confronting companies nowadays calls for advanced frameworks that link day-to-day operational reality with infrastructure capabilities. Connecting technological systems with corporate objectives across several industries, Enterprise Architecture has become an essential discipline. This article examines how Enterprise Architecture works synergistically with specialized domain knowledge in healthcare, manufacturing, and scientific research settings. Healthcare institutions face significant hurdles when deploying Electronic Health Record systems, requiring a careful balance between technical specifications and clinical workflow practicalities through a collaborative design that incorporates clinician perspectives throughout the architectural development process. Manufacturing industries undergo digital transformation through Industry 4.0 technologies, with cyber-physical systems and intelligent production frameworks gaining substantial advantages when production experts contribute to establishing monitoring parameters, alert thresholds, and analytics capabilities. Flexible yet organized architectures that support exploratory workflows are necessary in scientific study environments; these structures also enable broad data sharing and the allocation of computational resources across institutional barriers. Instead of secondary stakeholders, the collaborative model treats domain experts as active participants in architectural design, thereby producing answers that demonstrate both technical strength and practical applicability. Companies that utilize human-centric Enterprise Architecture techniques consistently demonstrate significantly better implementation success rates, higher user adoption, fewer post-deployment adjustments, and improved alignment of technical capabilities with operational demands. Sociotechnical systems theory and participatory design methodologies furnish the theoretical foundation for collaborative architectural development, acknowledging the interdependence of social and technical components within organizational settings. Effective enterprise architecture deployment requires constant communication between domain experts and technical architects, ensuring that technical frameworks serve human needs while leveraging computational power. This cooperative integration transforms Enterprise Architecture from a purely technological discipline into a strategic catalyst for operational excellence and organizational innovation, spanning healthcare, manufacturing, and research fields. While this article focuses on healthcare, manufacturing, and scientific research as primary case studies, the collaborative EA principles and methodologies discussed are broadly applicable across diverse industries, including finance, telecommunications, retail, government, energy, transportation, and education sectors.
Federated Learning for Industrial IoT Networks: Privacy-Preserving AI Across Distributed Manufacturing and Energy Systems
Industrial Internet of Things networks spread across manufacturing facilities and energy infrastructure generate enormous operational data streams. Traditional centralized processing creates bandwidth constraints and raises serious concerns about proprietary production information. Federated learning provides a transformative paradigm for collaborative model development without centralizing raw operational data. Data sovereignty remains protected while collective intelligence flourishes through distributed training mechanisms. Operational technology environments present unique challenges distinct from consumer-oriented applications. Equipment heterogeneity spans multiple manufacturers and machine generations. Data distributions vary significantly based on facility-specific operational patterns. Stringent cybersecurity demands require robust protection against adversarial manipulation within multi-party collaborative frameworks. The article presents comprehensive federated learning architectures engineered for industrial constraints and organizational complexities inherent within manufacturing and energy sector deployments. Hierarchical designs mirror natural industrial network structures through coordinated layers spanning edge devices, facility coordinators, and enterprise aggregation platforms. Advanced aggregation algorithms handle non-independent and identically distributed data patterns prevalent across manufacturing settings. Personalized learning mechanisms and clustering-based techniques address statistical heterogeneity challenges effectively. Differential privacy methodologies safeguard sensitive operational variables while maintaining model utility through adaptive noise calibration strategies. Blockchain-based coordination systems ensure transparent governance and equitable incentive distribution within industrial consortia. Byzantine fault-tolerant security protocols defend against malicious participants attempting to corrupt collaborative model development. Edge computing integration enables localized training and inference within resource-constrained industrial hardware deployments. Technical foundations emerge for privacy-preserving distributed intelligence supporting predictive maintenance, quality control optimization, and energy consumption prediction across diverse industrial ecosystems.
The Critical Role of Data Engineering in Modern Analytics
Data engineering serves as the cornerstone of contemporary analytics infrastructures, enabling organizations to efficiently collect, process, and deliver data throughout enterprise ecosystems. This technical article explores how data engineering has evolved from traditional batch processing paradigms to sophisticated real-time architectures that support mission-critical business operations across industries. The global datasphere continues expanding at unprecedented rates, with creation and replication significantly outpacing available storage capacity, creating both challenges and opportunities for data professionals. As organizations increasingly depend on timely, accurate insights for competitive advantage, robust data engineering practices have become essential strategic assets rather than merely technical capabilities. The article examines implementation methodologies, including pipeline automation, ETL/ELT processes, integration frameworks, orchestration platforms, and scalability considerations that form the architectural foundation of modern data ecosystems. Further sections explore cloud-based data engineering's transformational impact on operational economics, artificial intelligence's dependence on well-structured data infrastructure, and the quantifiable business impacts of mature versus underdeveloped data engineering capabilities. For organizations navigating digital transformation initiatives, understanding these fundamental principles and applications provides the foundation for leveraging data as a strategic asset.
Intelligent Trade Lifecycle Orchestration Using Agentic AI for End-to-End Trade Automation
The securities trading around the world is facing an increased complexity challenge of fragmented infrastructures, dependence on manual oversight, and exception-based management systems. The global trading ecosystem continues to undergo rapid digitization driven by algorithmic execution, cross-venue liquidity fragmentation, real-time settlement mandates, and increasingly complex regulatory obligations. Traditional process automation—consisting of process automation, static workflows, and rule-driven validation—offers productivity gains but lacks the adaptive intelligence to manage the dynamic, interdependent, and high-variability nature of trade lifecycles. Intelligent Trade Lifecycle Orchestration introduces an Agentic AIs orchestrating framework that operates autonomously and manages the establishment of trade, verification and matching, settlement and reconciliation, using self-learning, collaborating agents. The novelty of the framework lies in multi-agent architecture, dynamic policy learning, and predictive exception orchestration, making it possible to make changes in the workflow to move it to cognitive coordination. Multi-agent systems exhibit distributed problem-solving ability in which autonomous agents interact with each other via negotiations, cooperation, and competition to accomplish group goals that are beyond the reach of each agent. Deep learning reinforcement algorithms allow agents to improve policies as they are applied by observing the result of the policies, and find out the best strategies by learning them based on experience instead of adhering to rules. Asynchronous training techniques can enable the interaction of multiple agents with distinct instances of the operational environment at the same time, and can gain experience in a very short time frame with steady convergence characteristics. Event streaming systems' low-latency process high data volumes of trade events, updating market data, trade announcements, and settlement instructions as they occur. The Trade Ontology Layer defines standard semantics of trade status, settlement instructions, identities of counterparties, and regulatory requirements, so the interpretation of information can be common to all agents. Experimental assessments of anonymized data on institutional trading indicate that the trade exceptions, settlement latency, and the need to use manual intervention are significantly reduced relative to traditional robotic process automation and predictive machine learning models. The framework has a high level of audit traceability and has been proven to be more effective during market stress times due to its adaptive learning abilities. ITLO offers frameworks for building self-regulating financial ecosystems that are flexible, interpretable, and regulatory sufficient in changing market terms. Unlike classical automation (e.g., RPA, workflow scripts, rule-based engines), ITLO employs autonomous agents capable of negotiation, cooperation, competition, long-horizon reasoning, real-time learning, and cross-domain decision orchestration.
Developing Scalable Technical Readiness Frameworks: An Approach to Worldwide Engineer Capability Building
Multinational support operations frequently encounter difficulties maintaining uniform technical capabilities among dispersed engineering teams. This article presents actionable methodologies for constructing, expanding, and sustaining effective readiness initiatives targeting cloud engineering professionals. The article examines proven practices from extensive enablement programs, with particular attention to blended learning approaches, worldwide subject matter expert coordination, and iterative enhancement processes. A comprehensive readiness structure is introduced, merging modular educational components, practical scenario laboratories, and evidence-based feedback systems to guarantee flexibility and quantifiable learning achievements.
Event-Driven Platform Engineering: Building Always-On Consumer Systems with Exactly-Once Guarantees
Event-driven platform engineering has emerged as the foundation for modern always-on consumer systems across travel booking, ride-hailing, e-commerce, and financial services sectors. This article explores architectural principles for building platforms capable of processing high-volume event streams with sub-second latency, strict ordering guarantees, and exactly-once processing semantics essential for business-critical operations. Moving beyond traditional monolithic and batch processing paradigms, event-driven architectures provide loose coupling, temporal decoupling, horizontal scalability, and built-in resilience. The article explores log-based event ingestion, idempotent processing patterns, schema evolution strategies, and mechanisms for handling late-arriving data. It presents implementation techniques for achieving exactly-once semantics in distributed environments, including the transactional outbox pattern and change data capture pipelines. A reference architecture demonstrates these principles in action, with practical implementation guidelines covering design, deployment, and operational considerations.
Camera-Based Depth Perception for Precision Agriculture: A Software-Defined Approach to 3D Scene Understanding
Autonomous agricultural systems need strong three-dimensional perception skills to carry out specific tasks such as selective crop intervention, weed detection, and avoidance of obstacles. Existing solutions are based on active depth sensing (LiDAR/radar) that provides accuracy at the cost of significant economic, operational, and environmental factors limiting its use in a variety of farming environments. As this article will show, passive camera-based depth perception can perform equally as well at a fraction of the cost in hardware, power, and environmental sensitivity. The fundamental novelty is the systematic combination of multi-view stereo geometry, learning-based refinement, and confidence-conscious orchestration into a software-based system that is optimized to work in agricultural settings. This identification of passive imaging as the main sensing modality, as opposed to supporting active sensors, allows the ongoing performance improvement via algorithmic improvement and retrofit integration to already existing machinery, where redesigning hardware is economically infeasible. Multicrop species validation over several growth cycles using camera-only systems has shown that these systems can be used to provide acceptable depth accuracy, high temporal stability in the presence of mechanical vibration, high crop detection accuracy, and long field operation with long deployment cycles. These performance attributes, together with the huge economic benefits, make camera-based depth perception a viable and scalable alternative to active depth sensing as a means of precision agriculture.
Active-Active DNS Resiliency Across Two Providers: Architecture, Performance, and Operational Efficiency
Digital service delivery has undergone substantial transformation as organizations extend their operational footprint across international markets. Contemporary users anticipate seamless connectivity independent of geographic position, network volatility, or underlying infrastructure complications. Conventional single-provider or Active-Passive DNS failover methodologies demonstrate increasing inadequacy when addressing these requirements, primarily attributed to record propagation latencies, initialization challenges, and concentrated provider reliance. Active DNS implementation utilizing dual autonomous DNS providers resolves these constraints through concurrent traffic servicing from multiple operational endpoints while preserving provider-tier redundancy. Rather than establishing a primary DNS provider designation with secondary backup infrastructure, both providers simultaneously respond to queries, allocate traffic loads, and execute routing determinations. This dual-provider Active-Active framework achieves near-instantaneous recovery intervals, eliminates DNS provider dependency, and advances international performance through proximity-driven routing. Additionally, this article guarantees that complete deployed infrastructure participates in production operations rather than maintaining dormant status. This article investigates how dual-provider Active-Active DNS reinforces availability parameters, advances performance characteristics, and strengthens operational effectiveness through sophisticated routing mechanisms and resilient architectural frameworks.
Autonomous Platform Engineering: A Framework for Eliminating Human-Induced Latency in Cloud Operations
Many enterprise cloud operations today still rely on human remediation or ticketing, which can lead to increased time to resolution. This architectural effort is to move enterprise cloud operations from human-driven DevOps approaches to policy-driven, closed-loop autonomous operations. It seeks to eliminate talent shortages and queuing within hyperscale cloud growth by enabling self-healing autonomous infrastructure through agentic AI systems. These are goal-driven reasoning agents acting within a governance policy frame. Platform engineering is more than automating rules to remove manual tasks. Platform engineering enables intelligent agents to autonomously determine and execute scaling, security mitigation and compliance as a function of defined scenarios. Further, it separates autonomous versus human-in-the-loop configurations, and quantifies smart behavior. It establishes operational patterns with levels of performance that no human could achieve. By eliminating human-induced latency for routine operational activities and limiting human involvement to high-level decision making and non-routine situations, it enables operational velocity and operational reliability that are impossible with customary approaches. This article helps organizations prepare their environment to safely and effectively use autonomous systems against production workloads, including providing technical and governance frameworks.
Modernizing U.S. Federal Reserve Reporting Through Infrastructure-Led Transformation
The U.S. Federal Reserve reporting systems are a critical part of the financial infrastructure. They have to be perfectly accurate, strictly timely, fully auditable, and massively scalable. They should also absorb regulatory changes and infrastructure impacts. This article describes an infrastructure-led transformation model for Federal Reserve reporting platforms. Therefore, improvements made in the compute resources, data pipelines, storage infrastructure, and operational controls determine platform resiliency more than the reporting logic. These changes have resulted in substantial increases in processing throughput, reductions in reporting cycle time, and increased data accuracy levels. Furthermore, reduced unplanned downtime during peak regulatory reporting periods. Infrastructure engineering is an important driver of regulatory improvement and national economic viability. Modern platform architecture allows for much better accuracy, reliability, and growth in meeting national regulations, making compliance a built-in feature instead of just a series of tasks. Financial institutions gain capabilities to meet today's and future federal regulatory requirements and become better able to withstand operational disruptions with infrastructure-first architecture principles.
Agentic AI for Recruiting: Designing, Evaluating, and Governing Autonomous Hiring Workflows
Contemporary recruiting systems are reactive, relying on constant human labor for candidate outreach, interview coordination, and relationship building. Agentic AI offers revolutionary potential by providing autonomous agents that perform end-to-end hiring workflows while remaining compliant with organizational policies and legal constraints. The overall architecture decomposes recruiting into specialized agents responsible for sourcing, outreach, interview orchestration, screening analysis, feedback synthesis, and offer coordination, operating over shared memory with auditable actions and explicit guardrails. Retrieval-augmented generation grounds decisions in policy and role specification, while structured tool calls enable integration with applicant tracking systems and scheduling platforms. Human-in-the-loop controls support approval gates, uncertainty escalation, and bias-aware constraints. Evaluation includes efficiency metrics, quality assessments, compliance monitoring, and candidate experience measurement. Characteristic failure modes include hallucinated justifications, over-automation removing necessary human judgment, and unfair exclusion amplifying demographic disparities. The governance mechanisms are policy-as-code, end-to-end logging, end-to-end fairness, and minimalism of data. The framework can enable the scalable and reliable use of recruiting agents and accountability, transparency, and equity considerations required to support responsible automation in high-stakes employment situations.
AI Governance for Third-Party Models: The Compliance Blind Spot in Vendor AI
As organizations rapidly deploy AI to govern many core business processes, third-party and vendor AI models are increasingly used in healthcare, financial services, manufacturing, and public sectors. While organizations invest large budgets in governing their internally developed AI, vendor AI often lacks any governance, creating a compliance blind spot which can result in many risks and violations. This governance gap is particularly critical from a cybersecurity perspective, as vendor AI systems introduce unique attack surfaces including adversarial manipulation vulnerabilities, data exfiltration risks through model APIs, and supply chain security weaknesses that traditional security controls cannot adequately address. Regulatory regimes fully assign liability to organizations deploying AI, regardless of whether the organization developed the AI in-house, or purchased it. Because organizations lack technical access to the underlying components and external behavior of vendors' AI, they cannot scrutinize vendor AI for security vulnerabilities, model poisoning attempts, or backdoor exploits. This creates an intrinsic paradox: Organizations fully own responsibility for systems that they cannot fully control, audit for security flaws, nor understand. We propose a Third-Party AI Governance Framework based on systematic vendor AI classification based on decision criticality and regulatory ramifications, shared accountability architectures based on vendor-client responsibility, audit-ready transparency standards that require full documentation of all model attributes including security testing results, and continual oversight mechanisms beyond procurement-time evaluation. Organizations that cannot establish sufficient control over vendor AI security posture are likely to experience regulatory enforcement actions, security breaches, lawsuits, and loss of stakeholder trust. By contrast, showing and proving control of vendor AI through third-party AI governance may allow an organization to survive and even give it a competitive edge.
AI-Driven Predictive Analytics for Ethical and Efficient Credit Union Collections: Explainability, Fairness, and Member-Centric Recovery Optimization
Delinquent account portfolios present a major operational challenge in consumer finance businesses, requiring the trade-off of risk management and member experience. Static rules-based collections solutions focusing on retrospective (rather than potential) risk profiles often do not fit with an evolving risk profile, leading to inefficient use of resources and a poor member experience. This article describes how AI-based predictive analytics can improve credit union collections by more accurately predicting delinquency, using behavioral segmentation, and better targeting interventions in an ethical, regulatory-compliant, and responsible way. It outlines probability estimates, progression analysis, and actionable segmentation and intervention as foundational modeling approaches for creating predictive models. Explainable AI approaches have been advocated to ensure transparency and auditability, fairness monitoring protocols for algorithmic bias, and human-in-the-loop decision-making to maintain professional discretion and accountability. The evidence demonstrates that institutions achieve better operational results through their essential need to balance accurate predictions with their governance requirements. The operational improvements benefit members by creating better financial access and trust in the organization through its fair and open repayment methods, which focus on member needs.
From Monolith to Microservices: Architectural Strategies for High-Availability Database Re-platforming
The transition from monolithic architectures to microservices-based systems represents a fundamental paradigm shift in enterprise software development, particularly for organizations managing complex financial applications and database infrastructures. This article examines comprehensive architectural strategies for migrating legacy monolithic applications to distributed microservices architectures while simultaneously modernizing database platforms to achieve high availability and operational resilience. The article explores domain-driven decomposition methodologies that identify natural business boundaries and bounded contexts within existing systems, enabling effective service granularity decisions that balance independence benefits against distributed system complexity. Containerization and cloud-native technologies emerge as critical enablers, providing lightweight deployment mechanisms, orchestration capabilities, and dynamic resource allocation that significantly enhance system scalability and operational efficiency. Performance optimization techniques including query optimization, automated indexing strategies, polyglot persistence patterns, and infrastructure-as-code practices are analyzed to demonstrate how organizations can maintain optimal database performance across distributed microservices while managing data consistency and service dependencies. The article synthesizes research findings and empirical evidence to provide practitioners with actionable strategies for navigating the complexities of architectural transformation, addressing technical challenges in service orchestration, data architecture redesign, and resource management that are essential for successful microservices adoption in production environments.
Theoretical Frameworks for API-First and Shift-Left Quality Engineering in Microservices Architectures
Microservices architectures have shifted the model of how quality is assured. Specifically: end-to-end screening strategies used in monolithic architectures are not viable because UI centric approaches have delays in defect discovery; and resolving architectural defects post integration requires orders of magnitude more effort than resolving defects found at the level of service contracts. API first testing provides mechanisms for coping with the unmanageable exponential growth in possible test scenarios that arise from service dependencies in microservice architectures and other distributed systems. Contract-driven development specifies service interfaces in an executable way as provider-consumer contracts and generates testable contracts that enforce contract-test-first as an independent way to evolve and deploy services. Consumer-driven contract testing breaks up validating a contract into discrete steps, where consumer tests generate compatibility specifications as contract files and provider tests replay requests and responses against APIs. Defect seeding experiments show that an important percentage of integration defects can be detected in the consumer, provider, and HTTP-structural categories, although value range changes are a limitation of this method. The underlying economic rationale for shift-left quality activities is an exponential relationship between defect discovery delay and defect cost. Early-discovered defects are cheaper to fix than those found in a late phase of the software life cycle (after product deployment). Empirical studies suggest that shift-left quality activities can considerably lower defect levels, cost of rework, and lead-time, if a design-time validation philosophy is substituted for system test-time verification.
Secure Multi-Cloud Architectures for State Governments: A Governance-First Approach
State governments increasingly adopt multi-cloud strategies to enhance service delivery and reduce vendor dependencies, yet security governance remains fragmented across platforms. This article presents a governance-first architecture framework specifically designed for government multi-cloud environments that establishes centralized identity management, unified policy enforcement, and integrated compliance monitoring as foundational elements preceding workload deployment. The framework addresses critical security challenges, including fragmented identity and access management systems, inconsistent logging and monitoring capabilities, policy drift across platforms, and complex regulatory compliance requirements. Implementation follows a structured four-phase deployment strategy encompassing foundation establishment, identity integration, policy harmonization, and advanced governance capabilities. The governance-first architecture transforms traditional reactive security approaches into proactive governance-centered strategies that treat identity as the primary control plane for all multi-cloud operations. Technical implementation leverages cloud-native mechanisms while maintaining consistency through centralized governance policies that automatically translate into platform-specific configurations. Cost-benefit evaluation demonstrates substantial long-term operational benefits, including reduced security incidents, improved compliance audit efficiency, and decreased administrative overhead despite significant upfront investment requirements. The article concludes that governance-first architecture enables State governments to maintain security consistency while leveraging diverse cloud capabilities, ultimately supporting enhanced citizen services while meeting stringent regulatory requirements.
When Systems Are Uncertain: Human Reliability Engineering in AI-Driven Production
Existing work on reliability engineering is generally based on a deterministic model of the world, and interpretable modes of failure. Such assumptions are not valid for production AI systems, motivating the discussion of the model change from machine-centric to human-centric reliability constraints. The latter are defined by human operators' cognitive load, trust calibration, and interpretation of probabilistic signals. Challenges include cognitive overload from non-deterministic failure diagnosis, trust in automated incident response, alert design for probabilistic systems, investigation of opaque failures, and maintaining human agency in automation. These challenges illustrate that the reliability of AI systems requires a focus on human-machine cognitive collaboration, rather than simply a focus on performance. In this context, observability, alerting and automation can be reengineered to the cognitive limits of humans and to prevent skill loss. From a sociotechnical perspective, reliability can be understood as an emergent property of human-AI interaction. This provides the new basis for managing uncertainty in production environments beyond the limits of deterministic models.
Cognitive Middleware Orchestration: A Human-AI Framework for Distributed Data Consistency
With the advent of distributed microservices-based database architectures, polyglot persistence (where services are expected to be mostly independent from each other), and heterogeneous data stores, consistency across data stores has become a fundamental problem that existing tools do not adequately solve, particularly for larger context-driven business and compliance processes. This article develops a cognitive middleware orchestration framework for the symbiotic cooperation of artificial intelligence systems and human domain experts for resolving conflicts in data consistency within distributed systems. The proposed framework extends the customary orchestration with a clever middleware layer that observes the data streams and includes machine learning models to detect and classify conflict patterns. In conflict detection, interactive decision interfaces are provided to human experts. Suggested decisions to resolve each conflict are offered via the AI, and the human expert provides business justification for their chosen decision to support adaptive learning engines. As a result, the framework realizes progressive autonomy models, which increase the autonomy of the AI based on the assessment of the AI performance while keeping humans in the loop for the most critical decisions. The contributions advance the theory of consistency management and realize learning-enabled service meshes within cloud-native environments.
Digital Transformation of Mortgage Lending: Emerging Frameworks for Fractional Ownership, Subscription Payments, and Embedded Finance
Technological innovation is fundamentally transforming the residential mortgage business by removing conventional obstacles of accessibility of homes and service provision. Management systems based on gamification use the principles of behavioral economics for mortgage servicing and turn the routine payment into an interactive process that allows increasing borrower involvement and financial literacy and creates significant behavioral data. Fractional ownership models implemented with blockchain and distributed ledger technology and smart contracts enable the democratization of real estate investment by alleviating capital requirements and establishing markets of liquid property interests in small portions but still customized to regulatory frameworks and governance infrastructure. The payment systems of subscriptions combine the mortgage payments with the property taxes and insurance into fixed monthly payments, resembling models of consumer-oriented technologies services, which attract demographics that want to have stable housing expenses. Platform-based mortgage services directly integrate financing experiences into property discovery experiences via API-based integrations, creating a less transactional friction homebuying experience and speeding up the homebuying timeline. All of these innovations transform the nature of competition in mortgage industries, present new models of risk distribution, and are more digital infrastructures, which are driving the wider trends of financial technology with an emphasis on user experience, accessibility, and automated service provision.
Diagnosis and Foresight in the Design of Smart Charging Systems for Autonomous Electric Vehicle Fleets in Megacities
The rapid growth of electric transportation and the increasing adoption of autonomous vehicles have made the development of smart charging systems a necessity. Megacities in Iran face challenges such as the depletion of fossil fuel resources, rising air pollution, and infrastructural constraints, which further emphasize the need for advanced and optimized charging systems. This study examines the necessity of developing smart charging infrastructures for autonomous electric vehicles in megacities, analyzing the associated challenges and opportunities. The research focuses on evaluating the macro-environmental dimensions influencing this technology using the PESTEL framework, encompassing political, economic, social, technological, environmental, and legal factors. A descriptive-analytical methodology was employed, with data collected from secondary sources including scientific articles, industry reports, policy documents, and successful global case studies. The findings indicate that the development of smart charging systems requires coordinated planning among government entities, the private sector, and society. Economically, the high costs of installation and maintenance, along with currency fluctuations, are key challenges that call for financial incentives and foreign investment. From a technological perspective, integrating charging systems with smart grids, the Internet of Things (IoT), and artificial intelligence (AI) can enhance overall efficiency. Environmentally, the use of renewable energy sources and optimization of power consumption at charging stations are crucial strategies for mitigating environmental impact. Legally, the need for new regulations governing charging standards, cybersecurity, and data ownership poses significant challenges. This research provides a comprehensive analysis of the multifaceted development of smart charging systems and offers actionable insights for policymakers, urban managers, and investors to optimize charging infrastructure and advance toward sustainable urban mobility. The study underscores that for this technology to succeed in Iran, legal, economic, and social challenges must be meticulously managed, while technological and environmental solutions must align with broader sustainable development goals.
Consumption-Based Pricing in AI: Credit and Token Models for Scalable Monetization
Artificial intelligence service delivery has undergone a substantial transformation away from rigid licensing arrangements toward flexible consumption-driven monetization structures. Conventional flat-rate pricing schemes inadequately reflect the dynamic nature of AI computational demands. Resource requirements vary considerably based on model sophistication, inference intensity, and data handling complexity. Credit and token mechanisms function as standardized abstraction units. Such units separate billing processes from underlying infrastructure technicalities. Customers benefit from adaptable budget management capabilities through these monetization structures. Combined pricing models integrating committed spending with prepaid credit reserves enable greater operational flexibility. Overflow consumption calculations provide additional adaptability for variable demand scenarios. Implementation difficulties persist concerning pricing clarity and consumption unpredictability. Maintaining customer confidence remains a paramount consideration for service providers. The architectural underpinnings of consumption-oriented AI pricing receive thorough examination within the present article. Calibration techniques converting usage metrics into pricing units warrant detailed attention. Operational hurdles and ethical dimensions are addressed alongside supervisory frameworks, ensuring responsible deployment. The central contribution involves integrating technical pricing structures with transparency safeguards. Such integration facilitates responsible AI adoption scaling across varied implementation scenarios while preserving economic sustainability and stakeholder trust throughout the monetization lifecycle.
A Distributed Approach for Securing Healthcare Data
Digitization of healthcare has provided opportunities for improving patient care but also has brought with it major security vulnerabilities that could compromise the confidentiality, availability, and integrity of protected health information. This article reviews the proposed distributed system constructs for providing health data security between heterogeneous systems, organizations, and multiple institutions. It categorizes and reviews three approaches to distributed healthcare security: (1) Advanced Encryption Algorithms, including symmetric, asymmetric and homomorphic algorithms for encrypting health information-at-rest and in-transit, and key management mechanisms for secure access to cryptographic material across multiple nodes that may not be trusted; (2) Distributed Storage Systems, including distributed-ledger technology (DLT), distributed file systems, and fragmentation approaches for immutable patient consent and audit trail logging, redundancy to tolerate physical node compromise, and avoiding total infrastructure data loss due to localized security attacks; and (3) Access Control Mechanisms, including multi-factor authentication, role-based access control, attribute-based access control, federated identity management for distributed healthcare organizations, and patient access control and monitoring for distributed threat detection. The distributed model is now more attractive in modern health systems. Perimeter security models do not adequately protect health data. The health data moves through networks connecting hospitals, outpatient clinics, clinical research organizations, insurance companies, and third-party organizations. The proposed framework satisfies regulations according to HIPAA, the General Data Protection Regulation (GDPR), and the Health Information Technology for Economic and Clinical Health (HITECH) Act. The system can be performance optimized to balance between cryptographic strength and system responsiveness. The combination of encryption, decentralized storage, and access control provides defense-in-depth protection against cyberattacks. Future developments, such as artificial intelligence-enabled threat detection, quantum-resistant cryptographic algorithms and models for patient data control will shape how to create secure healthcare systems in our growing digital health networks.
Foundational Architectural Concepts for Distributed Telecom Infrastructure Platforms
Modern telecommunications infrastructure increasingly depends on distributed software systems to manage complex nationwide networks encompassing radio access, fiber transport, and service orchestration layers. As the telecommunications industry moves towards fifth-generation and later networks, the old, large-scale methods of planning and managing networks are not enough to handle the size, variety, and changing needs of today's systems. The transition of networks to support forthcoming services beyond fifth-generation and sixth-generation introduces important architectural challenges that force an evolution of existing operational frameworks. This article presents a comprehensive examination of foundational architectural concepts that enable the design and implementation of scalable, resilient, and adaptable telecom infrastructure platforms. The discussion encompasses modular domain-based architecture principles, scalability and fault isolation mechanisms, distributed data coordination strategies, and approaches for balancing consistency guarantees with operational continuity requirements. By articulating these concepts at a systems architecture level, this work provides a conceptual framework for understanding how large-scale telecommunications infrastructures can be engineered to support automation, intelligent optimization, and long-term technological adaptability without introducing prohibitive operational complexity. The architectural principles presented herein are implementation-agnostic and applicable across diverse telecommunications environments, offering guidance for practitioners and researchers engaged in the evolution of network infrastructure platforms.
Designing for Zero Downtime: Migrating a Legacy Gateway and Achieving High‑Availability Platform Engineering
Infrastructure migrations present substantial risks to service continuity, particularly for high-throughput SaaS environments. This article examines a gateway migration initiative at a leading enterprise SaaS provider, where a legacy Ruby/Sinatra monolithic gateway handling 50 million daily requests was migrated to a Kong Enterprise architecture. The migration employed phased rollout strategies, automated traffic replay for functional validation, and comprehensive infrastructure-as-code practices to achieve a seamless transition without service interruption. The initiative addressed multiple technical challenges, including the decoupling of tightly bound authentication and session logic into an OpenID Connect (OIDC) model, performance risk management during shadow traffic replication, and the enabling of rapid rollbacks. Results demonstrated successful service continuity throughout the transition period alongside operational improvements in reliability, reduced compute overhead, and enhanced security posture. This experience validates that detailed migration planning and incremental deployment strategies enable complex infrastructure transformations while maintaining strict Service Level Objectives (SLOs).
MLSecOps: A Comprehensive Framework for Secure Machine Learning Operations
This article introduces MLsecOps as an integrated framework combining machine learning operations with safety ideas to solve the particular flaws of the ML system throughout its development life cycle. Through rigorous verification methods, negative testing, and ongoing monitoring, MLSecOps offers methodical defense against data poisoning, negative attacks, and model drifts. Companies utilizing MLSecOps, compared to conventional safety solutions, experience security phenomena, fast threats, and superior models experience a substantial reduction in flexibility. Stating how MLsecops safeguards security systems against, the framework's efficacy is demonstrated by case studies in autonomous vehicles, e-commerce suggestions, and healthcare diagnostics. This helps AI to be accountable in high-day scenarios by extending DevSecOps concepts. Applications where safety breakdowns could have major repercussions.
Self-Healing Data Quality Pipelines in Cloud-Native Architectures Using Event-Driven Learning
Data quality failures impose significant financial burdens on cloud-native enterprises through downstream transaction failures, compromised customer experiences, and extensive manual remediation. Traditional rule-based validation and centralized cleansing pipelines cannot address distributed system characteristics: fragmented microservices ownership, asynchronous failure manifestation, context-dependent correctness, and informalized recurring remediation patterns. This article proposes a self-healing data quality framework treating quality outcomes as observable events within event-driven architectures, enabling continuous learning and adaptive remediation while preserving governance controls essential for regulated environments. The framework implements domain-aligned microservices with a layered architecture separating deterministic validation from learning augmentation. Event streams capture quality signals enabling pattern recognition, confidence-based decisioning, and automated correction. Feedback loops incorporating downstream outcomes and human overrides continuously refine learning models. Embedded governance mechanisms ensure explainability, auditability, and controlled automation through decision traceability, version logging, and role-based thresholds. Operational metrics prioritize reducing repeat failures and manual effort over isolated model accuracy, transforming data quality into adaptive system capability, maintaining regulatory compliance and stakeholder trust.
Decision Latency as a First-Class Performance Metric in AI-Native Engineering Organizations
The artificial intelligence features have essentially revolutionized the software development speed with technical implementations that took hours to minutes. State-of-the-art code generation systems can generate useful implementations at speeds never before seen, but the throughput of organizations is limited by decision-making architectures targeted at a lower-speed execution paradigm. Elite engineering organizations show frequencies of deployment and orders of magnitude better in lead times than low performers, which is largely due to the latency of decisions, compared to technical capability. The given framework defines decision latency as a performance metric of operations, which includes recognition latency, coordination overhead, approval queue, and implementation delays. Architectural designs are used to manage this limitation by distributing decision rights, automation using policy-as-code, synthesizing context, and progressive authority models, which balance governance demands with performance speed. Trackable proxies such as the approval queue time, number of review loops, frequency of escalation and rework churn allow systematic measurements to be made across organizational settings. There are different manifestations of decision architecture challenges in enterprise cases involving financial services, cloud infrastructure, and healthcare technology. To ensure the transformation of AI-enabled execution speed into a consistent organizational throughput, accountable authority structures, validation procedures, and coordination protocols need to be redesigned accordingly to align with the modern development capabilities without compromising accountability and risk management corresponding to the severity of consequences.
Root Cause Analysis of Repeated Flange Leaks in High-Vibration Piping Systems
Industrial flanged joints in high-vibration piping systems represent critical sealing interfaces where mechanical integrity directly impacts operational safety, environmental compliance, and economic performance across petrochemical, power generation, and manufacturing sectors. Vibration-induced failures originate from rotating equipment excitation, flow-induced turbulence, and structural resonance phenomena that create dynamic loading conditions exceeding static design assumptions. Traditional maintenance practices focusing on component replacement fail to address fundamental mechanical causes, resulting in recurring failure patterns despite substantial investment in repairs and upgrades. This article presents systematic diagnostic techniques integrating field measurement capabilities with computational modeling to identify root causes of repeated flange leakage. Engineering solutions encompass vibration control methodologies, material upgrades, structural modifications, and predictive maintenance strategies that address excitation sources, transmission paths, and system response characteristics. Industrial case examples demonstrate measurable reliability improvements through comprehensive approaches that combine vibration assessment, targeted remediation, and optimized maintenance procedures. The integrated framework enables proactive intervention before failure occurrence while providing quantitative foundation for engineering decisions that enhance long-term asset performance and operational sustainability.
Driving Agility through Microservices: Success in Financial Technology
Microservices architecture represents a transformative paradigm for financial technology applications, addressing critical challenges inherent in traditional monolithic systems. This architectural pattern decomposes complex applications into independent, self-contained services that encapsulate discrete business capabilities. The modular design enables organizations to achieve enhanced deployment agility, significantly reducing time-to-market for new features and security updates. Service independence facilitates technology diversity through polyglot persistence, allowing teams to select optimal tools for specific requirements. Fault isolation mechanisms ensure system resilience, preventing cascading failures that typically plague monolithic architectures. Granular scalability permits precise resource allocation aligned with actual demand patterns, optimizing infrastructure costs particularly in cloud environments. The architecture seamlessly integrates emerging technologies including artificial intelligence, blockchain, and real-time analytics without disrupting core operations. Autonomous teams maintain end-to-end ownership of individual services, accelerating innovation cycles and fostering organizational agility. Financial technology organizations leveraging microservices achieve superior responsiveness to market dynamics, regulatory changes, and evolving customer expectations while maintaining robust system reliability and operational efficiency throughout their digital transformation journey.
Streaming Data Approaches for Prescription Path Tracking: Design Principles, Measurement Frameworks, and Therapeutic Access Implications
The increasing complexity of advanced therapeutics and distributed care delivery makes prescription path coordination a central determinant of timely medication access and clinical outcomes. Prior authorization and coverage verification introduce substantial friction; recent physician surveys report that 94 percent of clinicians see these processes delaying necessary care and that 78 percent observe patients abandoning treatment when they encounter administrative barriers. Across specialty and community pharmacy networks, stakeholders typically lack consolidated, real‑time visibility into prescription status as cases move between prescribers, payers, patient support programs, and dispensing sites. Streaming prescription path hubs address this visibility gap by continuously capturing clinically and administratively meaningful events and propagating status changes with low latency to connected platforms and care teams. Treating the prescription path as a longitudinal patient journey rather than a collection of isolated transactions enables organizations to compute metrics such as time to therapy initiation, phase‑specific delays, and rework rates. This article synthesizes streaming platform patterns and health information system principles to propose design concepts, implementation strategies, and evaluation approaches for prescription path hubs. The resulting blueprint is aimed at healthcare technology architects who must design platforms that simultaneously support operational coordination and measurement of therapeutic access, enabling systematic monitoring, optimization, and computational analysis. Moreover, these hubs create unified information bases supporting cross-channel communication centers and mechanized workflow synchronization spanning prescription and coverage procedures, holding significance for persistence enhancement, bureaucratic load diminishment, and health fairness progression.
Natural-Language Finance Agents: Architectures, Governance, and Human–AI Collaboration for Enterprise-Scale Analytics
Enterprise financial analytics faces critical accessibility barriers where complex data systems require specialized technical expertise, creating bottlenecks for non-technical stakeholders seeking analytical insights. This article presents a comprehensive framework for natural-language finance agents that addresses architectural, governance, and collaboration requirements for production deployment in regulated financial environments. Our framework proposes constrained generation mechanisms with financial domain-specific semantic modeling, policy-binding governance structures with automated compliance validation, and structured human-AI collaboration patterns with escalation protocols. We demonstrate framework applicability through illustrative use cases spanning financial forecasting, variance analysis, and executive reporting while maintaining regulatory compliance and audit trail requirements. The proposed architecture defines evaluation frameworks for analytical accessibility while preserving accuracy standards essential for enterprise financial decision-making.
Feature-Level Fusion of T2- and PD-Weighted MRI Using Vision Transformer for Five-Stage Alzheimer’s Disease Classification
Alzheimer’s disease (AD) is a progressive neurodegenerative disorder in which early and accurate diagnosis remains challenging, particularly for clinically overlapping mild cognitive impairment (MCI) stages. Although magnetic resonance imaging (MRI) is widely used for AD assessment, reliance on a single imaging sequence often limits discrimination between disease stages. Feature fusion has recently emerged as an effective strategy to enhance classification performance while maintaining clinical feasibility. In this study, an efficient feature-level fusion framework is proposed that integrates complementary information from T2-weighted and proton density (PD-weighted) MRI. Deep features are extracted exclusively using a Vision Transformer (ViT) model and combined through a weighted fusion strategy, followed by k-nearest neighbors classification. Experiments conducted on the ADNI dataset demonstrate that the proposed approach achieves high discriminative performance across five classes (AD, CN, EMCI, LMCI, and MCI), reaching an overall accuracy of approximately 99.35%. The fusion framework consistently outperforms single-modality models, particularly in distinguishing transitional stages. These results indicate that ViT-based feature fusion of T2 and PD MRI provides a robust and computationally efficient solution for multi-stage Alzheimer’s disease classification, with strong potential for practical clinical application.
Organizational Climate in Higher Education: A Comparative Study of Academic and Administrative Staff Perceptions.
The present study explores differences in organizational climate perceptions between academic and administrative staff in private higher educational institutions in Nagpur City. A descriptive and analytical quantitative research design was adopted, and primary data were collected from 78 respondents representing 12 institutions using a structured questionnaire. Normality testing confirmed non-normal data distribution, leading to the application of the Mann–Whitney U test for comparative analysis. The findings indicate statistically significant differences across major organizational climate dimensions, with academic staff reporting more favorable perceptions related to leadership support, communication, and professional autonomy. In contrast, administrative staff expressed relatively lower satisfaction due to limited participation and recognition. The study concludes that inclusive leadership practices, participative decision-making, and balanced recognition systems are crucial for fostering a supportive organizational climate and enhancing employee engagement in private higher education institutions.
AI-Driven Cyber Risk Management in Adaptive Threat Environments
The fast integration of generative artificial intelligence (AI) into the work of cybersecurity has radically changed the face of cyber threat in modern companies. Attackers are now able to automate reconnaissance, create adaptive malware, and perform large-scale and individualized social engineering, and defenders are now increasingly relying on AI-based detention, automated response, and security coordination. The convergence has resulted in a rapidly changing adversarial environment where classical governance and risk management frameworks of cybersecurity are becoming less effective. This paper suggests a framework of engineering management of governing AI-enabled cyber risk, comprising adaptive risk evaluation, responsible AI governance, and strategic investments prioritization. The research paper uses a mixed-method design science methodology, which is based on the conceptual framework formulation, synthesis of the findings of the AI red-team and blue-team simulation studies conducted in previous research and qualitative synthesis of the reported individual industry cases. The framework describes how the management processes in engineering have to change as attackers and defenders switch to using generative AI, and introduces dynamical risk measures, multi-functional governance systems, and lifelong learning systems to aid adaptive defense. Findings can be used in the area of cybersecurity governance, engineering management scholarship, and offer guidance in practice that can be utilized by practitioners and policymakers to minimize AI-related cyber risk and maintain organizational resilience.
A Data-Driven Analysis of Electromagnetism Simulation Platforms: Thematic Coverage, Accessibility, and Structural Patterns
Digital simulations have become a central resource in electromagnetics education due to their capacity to support the visualization and exploration of abstract phenomena. Despite their widespread use, systematic analyses examining their thematic coverage, technical characteristics, and accessibility remain limited. This study aims to provide a comprehensive overview of the current landscape of electromagnetism simulation platforms, identifying patterns in content coverage, technological design, and accessibility features. A mixed-methods, data-driven approach was applied to a dataset of 43 simulation platforms. The analysis combined educational data mining, natural language processing (NLP), correlation analysis, and clustering techniques to evaluate variables related to animation type, programming environments, language availability, licensing models, accessibility support, and emerging technological features. NLP-based thematic analysis was used to compare simulation content with standard electromagnetism curricula. The results reveal a strong emphasis on phenomenological and application-oriented topics, with limited representation of core theoretical constructs. Correlation analysis identified associations between language availability, licensing models, and technical complexity, while clustering techniques revealed distinct thematic groupings and patterns of content concentration across platforms. The findings highlight both the pedagogical potential and the structural gaps present in current electromagnetism simulation resources. This study contributes a replicable analytical framework for evaluating digital educational content and offers evidence to support more informed selection and development of simulation tools in physics education.
Health Care Expenditure, Health Outcomes and Economic Growth in Selected West African Countries: Evidence from Panel Data
This study used panel analysis to examine the effects of healthcare expenditure on economic growth in selected West African countries from 1990 to 2024. We outlined three specific objectives in a bid to achieve the broad objective of the study, which include, to examine the effect of healthcare expenditure on economic growth in selected West African countries, to investigate the impact of per capita health expenditure on some selected health outcomes (life expectancy, infant and maternal mortality rates) and to investigate for any causal relationship between health expenditure and economic growth in selected West African countries. Data employed for the study were sourced from the World Bank Databank and World Development Indicators (WDI). The study used the Pooled Mean Group Auto-Regressive Distributed Lag (PMG-ARDL) to estimate the model, given that the annual data used for the model estimations were integrated of order I(1) and I(0). Our ARDL long-run estimation revealed that both healthcare expenditure and life expectancy have positive and significant effects on economic growth. We found no short-run relationship amongst our variables and a uni-directional causality relationship between economic growth and healthcare expenditure. This study establishes that in general, healthcare expenditure has both positive and significant long-run effects on economic growth in West Africa. In view of the above, the study recommends that governments in West Africa should prioritize increases and sustained investments in the healthcare sector as a long-term economic development strategy. Keywords: Health care expenditure, Life expectancy, Infant mortality rate, maternal mortality rate, Economic growth
Modernizing Regulatory Licensing Systems: A Scalable Digital Transformation Framework for Public Sector Agencies
The regulatory bodies in the public sector are under pressure to ensure that they modernize the old licensing regimes but at the same time ensure strict adherence, audit and statutory control. Historical licensing processes, mostly based on paper and stand-alone databases, cannot meet modern requirements for speed, transparency, and interoperability. Government organizations are facing increasing demands from citizens, regulated professionals, and governing bodies to provide services that are equivalent to the digital experiences offered by the private sector while also maintaining high levels of statutory adherence. The legacy systems usually have disjointed databases, handwritten document processing bottlenecks, and largely lack integration tools that limit information sharing with the partner agencies. The article offers a flexible plan for digital transformation that draws on the practices of large companies, which includes using cloud services, automating workflows, standardizing data, and coordinating across different jurisdictions. The proposed framework combines rules for managing the system, gradual updates, and a focus on the needs of those involved to reduce risks in operations and create a chance for noticeable improvements in efficiency. In contrast to the generic enterprise approaches, this one deals with unique challenges of the public sector, such as statutory requirements, budgetary cycle commitments, political shifts, and regulatory requirements, which need special transformation strategies. The model focuses on gradual change rather than replacing the entire system, considering organizational capacity limitations and risk aversion that are inherent in government settings. Empirical results indicate that there are decreases in processing cycle time, more transparency in regulations, and enhanced interoperability in state systems and federal systems. The framework facilitates the use of incremental value delivery of modular architectural elements, which permit agencies to modernize select capabilities without interfering with the current operations. Patterns of implementation show uniform efficiency gains in various regulatory areas and standards of audit compliance and confidence in stakeholders. The article adds a practical model that can be applied by government and compliance-oriented entities to the implementation of complex digital licensing modernization projects and a practical guide that will be used to balance innovation and regulatory compliance.
Architectural Framework for Regulatory-Compliant Enterprise Co-Branded Credit Card Platforms
Enterprise co-branded credit card projects must work within strict rules about regulations, security, and privacy, while also handling a large number of transactions. They need to follow different compliance rules set by various organizations, especially because they involve both banking and non-banking large consumer systems. The principles of security-by-design and compliance, which involve integrating security and compliance into the system from the outset, help the platform meet regulatory requirements at a high level and enable flexible processing through an event-driven approach for making credit decisions. of concerns and operational scalability. Establishing a segmented cardholder data network ensures the separation of cardholder data from other system components. Tokenization modules create tokens to validate transactions and credentials for the credit card in credential vaults. Privacy-preserving techniques reduce the identity, attribute, inferential, and membership disclosure threats. Privacy by Design principles provide guidance for protecting privacy systems proactively instead of attempting to patch privacy issues as they arise. High-performance network designs allow for handling many transactions at once while being adaptable based on available resources and the type of work needed. The circuit breaker pattern and replica management capabilities ensure failure does not bring down a service.
Measuring Retrieval Freshness and Accuracy Degradation in Continuous ETL‑Driven RAG Systems
Retrieval‑augmented generation systems have transformed enterprise deployments by grounding large language model outputs in external documents, yet their effectiveness degrades significantly as underlying corpora evolve through continuous ETL pipelines and streaming ingestion. The CRAG benchmark reveals fundamental performance gaps where even optimized industry-grade RAG architectures fail substantial portions of temporally sensitive queries, while the HOH benchmark demonstrates that outdated information causes catastrophic accuracy losses and can render retrieval augmentation counterproductive compared to standalone language models. RAGBench enables component‑level decomposition, separating retrieval quality from generation fidelity across extensive labeled examples, while Streaming RAG establishes that incremental real‑time updates yield measurable recall improvements with production‑grade latency and throughput characteristics. Dense vector retrievers have an issue in maintaining fresh embeddings, which hybrid architectures between semantic and lexical signals have shown to be more resilient in the face of staleness. Empirical evidence across benchmarks confirms that retrieval freshness constitutes a first‑order determinant of end‑to‑end accuracy and hallucination behavior, with degradation effects sufficiently severe to eliminate or reverse the value proposition of retrieval augmentation under conditions of substantial staleness. These results prove that companies that use RAG in dynamic settings should introduce cost-sensitive and domain-aware update methods that balance between computational costs and accuracy demands. The transition from ad hoc refresh policies to principled freshness management becomes essential for maintaining reliable performance as retrieval indices diverge from evolving source systems.
UALink, Ultra Ethernet, and PCIe: Transforming Next-Generation HPC and AI Workloads
As HPC and AI workloads fundamentally transform data center architectures, the demand is growing for heterogeneous compute infrastructures with unprecedented bandwidth, low latency, and massive parallelism. Legacy interconnect technologies, such as InfiniBand and Ethernet, can no longer address the scale and diversity of the AI training and scientific simulation workloads deployed today. Complementary vendor-neutral, open standards include UALink, Ultra Ethernet, and next-generation PCIe with Compute Express Link (CXL), forming a set of hierarchical interconnect technologies that are optimized for future compute ecosystems. UALink provides multi-terabit throughput at sub-microsecond latency in vendor-neutral, flexible topologies with optimal GPU placement and contention-free configurations. Ultra Ethernet transforms Ethernet networking protocols with deterministic forwarding, advanced congestion control, and hardware-accelerated collective communication primitives to enable commodity Ethernet as a low-latency fabric suitable for scale-out AI use cases while retaining Ethernet protocol compatibility. PCIe evolution, through Compute Express Link (CXL), allows heterogeneous memory architectures with cache-coherent, memory-mapped memory, including Managed DRAM, ReRAM, and persistent non-volatile memory. Managed DRAM with hardware-managed tiering between classes of memory has demonstrated order of magnitude performance improvements compared to software-managed tiered memory systems. These interconnect technologies address green computing issues across the hardware life cycle and enable the topology-aware scheduling, data migration, and resource composition that will be important in future cloud platforms that handle heterogeneous workloads. The success of these convergence architectures will ultimately determine whether cloud-based data center architectures will cope with the computational demands of future AI, natural language processing, and scientific discovery workloads, such as climate modeling.
Building High-Reliability Terminal Automation Systems: Safety, Throughput & Operational Excellence
Modern oil and gas bulk liquid terminals rely on highly integrated hardware, software, and intelligent control systems to manage the complex operations of receiving petroleum products via ships and railcars, storing them in tank farms, and distributing them through truck loading facilities to retail outlets and end customers. High levels of reliability to protect personnel, physical assets, and environmental resources through Safety Instrumented Systems (SIS), interlocks, and emergency shutdown (ESD) systems are essential requirements that must comply with international standards including IEC 61511 and ISA-84. These platforms implement and automate custody transfer workflows for crude oil, refined products, LNG, and LPG, provide real-time visibility to critical operating parameters including tank levels and flow rates, and use predictive analytics to recommend maintenance schedules for pumps, loading arms, and electrical equipment. This results in improved asset availability and operational continuity for terminal operators. Contemporary operational technology platforms connect to enterprise information systems spanning tank inventory management, order fulfillment, truck queue management, billing, and regulatory compliance reporting. Smart terminals employ artificial intelligence, digital twins, and Internet of Things devices to enable self-optimization of loading operations and predictive control of tank farm logistics. This article presents practical implementation approaches including brownfield SIS deployment challenges, phased rollout strategies, and industry-standard key performance indicators. Real-world examples demonstrate typical operational improvements including reduction in demurrage costs, increased throughput efficiency, improved custody transfer accuracy, and decreased unplanned downtime.
AI-Driven Engineering Challenges in PCI-Compliant Mobile IoT Shipping Systems
Enterprise shipping solutions for large enterprises have evolved into a distributed network of mobile IoT endpoints that coordinate payment processing, package handling, and shipping label printing through a global network of regional hubs. The article discusses the engineering challenges of incorporating artificial intelligence into PCI-compliant mobile shipping platforms that are separated by a very strict security perimeter to provide predictive optimization without exposing cardholder data. The mobile revenue capture, hardware state management, and regulatory compliance systems do not allow for customary reactive engineering at scale. AI acts as a predictive engineering layer that lives outside the payment environments and takes telemetry from the hardware, transaction data, and workflow context to identify the causes of future instability before they happen. This shows how clever automation and regulatory compliance can co-exist when systems are designed to deliver measurable increases in transaction success and operational efficiencies in distributed, high-volume environments.
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.
Interoperability Standards in Modern Workflow Orchestration Systems
Modern cloud workflows often involve interoperability between orchestration systems from different vendors, eased through interoperability contracts for event formats, telemetry protocols, data lineage, and artifact packaging. Orchestration systems can be classified across 5 architecture layers spanning workflow definition interoperability, execution state, integration protocols, observability, and deployment packaging. CloudEvents provides vendor-neutral envelopes for events that can flow between event brokers; OpenTelemetry provides vendor-agnostic telemetry data collection to and from user code in applications, using standardized APIs, SDKs, and OTLP. OpenLineage supports dataset interactions at orchestration boundaries, useful for data governance and impact analysis. The OCI Image Specification enables portable container execution across runtime environments. Kubernetes CustomResourceDefinitions extend platform capabilities to support built-in lifecycle management features and declarative tooling. Service reliability governance uses Service Level Objectives and error budgets to drive tradeoffs between availability and operational cost, preventing organizations from confusing internal objectives with Service Level Agreements with customers when delivering application reliability. Performance benchmarks indicate that the OpenTelemetry Collectors can be scaled up to thousands of events per second across different cluster configurations, maintaining low average latencies and resource consumption. Organizations in the full observability stage exhibit radically improved mean time to detection and mean time to resolution, deploy several times a day, and have change failure rates under five percent. Contract-first architecture enables the decomposition of monolithic workflow platforms into pluggable components with stable, versioned interfaces that do not cause vendor lock-in and can evolve the ecosystem across a container cluster, data pipelines, and service architectures via open APIs.
Generative AI for Early Disease Detection: Hybrid ViT-CNN with Multi-Head Attention in Medical Imaging
Early recognition of disease in medical imaging gives a good chance for fast treatment and an increase in survival in serious cases like brain tumors. To better classify brain tumors from MRIs, our study introduces a new type of deep learning method that combines CNNs and ViT for improved results. Though strong at finding out small features in an image, traditional CNNs fail to notice big relationships in the image because their receptive fields are narrow. Instead, ViT brings in multi-head self-attention to allow the model to focus on long interactions between different parts of the image. By mixing the two types of architectures, this research uses CNNs to zoom in and ViTs to look at the full picture. The study makes use of a brain tumor MRI dataset that anyone can access for free from Kaggle. Normalization, resizing, and augmentation methods were all utilized to increase the model’s strength and ability to generalize. A stratified 80-20 data split was used to develop and verify the hybrid model. The clear boost suggests that adding local detail perception from CNN and the global influence of ViT can help our model better interpret medical images. Adding Grad-CAM showed us which parts of the scans were the most important to the decision made by the model, making the overall behavior of the model more obvious. Overall, this work establishes that CNN-Transformer architectures are helpful for medical imaging and prepares the way for introducing AI-assisted diagnostics in clinical practice to aid faster choices.
AI-Driven Cybersecurity for Detecting Anomalous API Access Patterns in Distributed Applications
Application Programming Interfaces (APIs) are what make modern distributed and cloud-native apps work. They let loosely connected services work together without any problems. But the fact that APIs are so widely used has made them much easier to attack, making them perfect targets for stealthy and behavior-driven cyberattacks that can get by traditional signature-based defenses. Current methods for detecting anomalies often either use supervised learning, which needs a lot of labeled attack data, or only use unsupervised learning, which doesn't have enough power to tell the difference between classes and has a lot of false positives, especially when there is a lot of class imbalance. This research presents an AI-driven Normality Fusion Hybrid Model to identify aberrant API usage patterns in distributed systems. The suggested method uses unsupervised anomaly modeling to learn normal API access behavior and combines deviation-aware signals into a supervised classification pipeline. An Isolation Forest is used to model the normal behavior manifold and make deviation scores, which are then normalized and combined with the original access features. A class-weighted Histogram-based Gradient Boosting classifier is then trained on the improved feature space to find rare and changing anomalies in a strong way. To fix the problem of very uneven class sizes, Random Over-Sampling and the Synthetic Minority Over-sampling Technique (SMOTE) are used during training. Numerous trials on the API Security: Access Behavior Anomaly Dataset show that the suggested model works better than strong baseline models and contemporary state-of-the-art techniques, with an overall accuracy of 99.87%. The results show that normality-aware feature fusion greatly improves detection robustness, interpretability, and generalization. This makes the proposed framework good for real-time API security monitoring in remote situations.
The Operation and Control Strategy of Microgrids: A Brief Review
A microgrid is an effective approach for integrating various distributed energy resources to meet local energy demand. It possesses the capability to operate independently or in conjunction with the main utility grid. Typically, a microgrid is a small-scale power system (usually several megawatts or less) comprising three primary components: the ability to function in both grid-connected and islanded modes, distributed power generation sources, and autonomous control centers. Microgrids enhance system resilience by ensuring uninterrupted operation during grid disturbances. Additionally, they are environmentally sustainable and contribute to improved power quality. As a crucial element of modern power systems, microgrids can effectively mitigate peak load demands on the utility grid. The incorporation of renewable energy sources within microgrid frameworks significantly reduces CO₂ emissions. However, achieving optimal performance requires precise control techniques to balance energy supply and demand. This review article provides a comprehensive analysis of microgrid operations and control strategies. The first section examines the fundamental principles of microgrid operation, while the second section evaluates advanced power control strategies, with a particular focus on multi-agent control methods for microgrids.
Leveraging Generative AI to Build Custom Engineering Apps in PLM Ecosystems
PLM (Product Lifecycle Management) systems customarily represent a highly rigid, monolithic Systems-of-Record for engineering and product-related data. The emergence of Generative AI-powered Low-Code/No-Code development platforms presents a transformative opportunity to evolve these legacy architectures into adaptive Platforms of Apps specifically designed for Digital Twin implementations. This architectural transition enables domain experts—including mechanical engineers, quality specialists, and production managers—to create purpose-built micro-applications such as geometric dimensioning and tolerancing validation tools, first article inspection apps, corrective action tracking systems, Overall Equipment Effectiveness dashboards, and predictive maintenance solutions without requiring formal programming expertise. The Platform of Apps model separates core data management functions from application-layer innovations, establishing PLM as a foundational services infrastructure upon which diverse engineering applications can be rapidly developed and deployed. By integrating real-time data synchronization, machine learning-based predictive analytics, and prescriptive maintenance recommendations, organizations can transform passive data repositories into active optimization engines. This paradigm shift addresses critical integration challenges in traditional PLM deployments while establishing technical foundations for comprehensive Digital Twin architectures in regulated manufacturing environments.
Machine Learning Model Deployment and Monitoring in Google Cloud Platform (GCP): A Scalable and Reliable MLOps Framework
Machine learning models have to move beyond the experimental platform to a production platform where they can produce real business value, but this shift is difficult because the infrastructure is complex, and organizational concerns continue to complicate the change, not to mention the ongoing issue of model performance as data distributions change over time. Vertx AI is one of many managed services offered by Google Cloud Platform, which also includes Cloud Run, BigQuery, Dataflow, and Cloud Monitoring that can be used in combination to enable a full range of MLOps practices to deploy, monitor, and maintain machine learning systems at scale. Various architectural designs, such as online prediction models to provide real-time inferences, batch models where the throughput of data is high, and stream architecture where data flows continuously, need to be carefully considered with the deployment of production ML systems. Continuous monitoring is important since the models degrade and require automated detection systems, which can detect that performance is worsening before it affects the users. Automated retraining pipelines with human intervention in critical decisions can help organizations to keep models accurate and also ensure that governance and compliance requirements are upheld. Schema validation, centralized stores of features, security measures, and cost optimization initiatives are best practices that help organizations to design ML systems that are reliable, precise, and economically feasible in their lifecycle of operations.
Prompt Engineering or Prompt Fraud? Governance Challenges for Audit
Generative Artificial Intelligence (GenAI) has rapidly become a transformative tool across business functions, including finance, internal audit, and compliance. However, its adoption introduces novel risks that existing frameworks are not fully equipped to address. This article defines prompt fraud as the intentional manipulation of AI prompts to produce outputs that bypass traditional internal controls and generate misleading or fraudulent artifacts. Unlike conventional fraud, which targets systems or personnel through established attack vectors, prompt fraud exploits linguistic controls at the reasoning layer of GenAI systems. The concept represents a paradigm shift in how fraud can be perpetrated, as it requires no system-level intrusion, no credential compromise, and no technical exploitation of software vulnerabilities. Instead, it uses the natural language features of large language models to create responses that sound convincing, include false information, or tell misleading stories meant to trick auditors and decision-makers. This article explores the evolving threat landscape surrounding prompt fraud, provides a structured audit framework for its detection and prevention, assesses the control weaknesses that make organizations vulnerable, and proposes mitigation strategies grounded in governance, technology, and human oversight. The paper further examines the roles of internal and external threat actors, the implications of Shadow AI, and the regulatory and ethical dimensions of AI-assisted fraud. It ends by suggesting that organizations should use better audit methods, strong AI management systems, and ongoing monitoring to deal with the fast-changing risks from GenAI in business settings.
The Role of SRE Governance in Ensuring Ethical and Transparent Digital Service Delivery
Digital services used in financial transactions, healthcare provision, education or civic engagement represent critical infrastructure. This means that site reliability engineering must also adhere to ethical, institutional and social dimensions that transcend technical service level objectives and related metrics. While SRE's typical definition of service reliability involves operational metrics of uptime, latency, and errors, there are equity and digital opportunity dimensions to reliability. This article reconsiders SRE as a governance discipline grounded in structures of accountability. The Multi-Layer SRE Governance Model is a novel governance model composed of five inter-related layers. It comprises: normative reliability commitments (SLOs) as socio-technical reliability obligations; policies and controls forming a domain of decision rights and monitoring authority; operational transparency protocols to build trust in incident communication; architectural equity design to provide fairness in distributed systems design; and algorithmic accountability frameworks to govern automated remediation systems. With roots in multi-disciplinary service level management, distributed systems architecture, fairness in resource allocation, and algorithmic accountability, the model articulates how assumed distributions of acceptable failure, prioritized recovery, and differential impact on users are reflected in reliability engineering practice. The governance model advances actionable implementation recommendations to support embedding transparency, auditability and equity in cloud-native and DevOps. In this way, we position SRE governance as an emergent cross disciplinary field at the intersection of distributed systems engineering, digital infrastructure ethics and institutional accountability to support service equity.
AI-Augmented Verification for Next-Generation VLSI Systems: Challenges, Techniques and Future Directions
The verification problems in the semiconductor industry have never been more of a challenge as the System-on-Chip architectures bring in heterogeneous computing units, sophisticated memory hierarchies and various interface standards into an ever-more complex silicon implementation. Design verification has become the most popular bottleneck in semiconductor development, consuming large amounts of engineering resources and failing to cope with the exponentially growing state-space and protocol diversity. Artificial Intelligence can provide a groundbreaking direction with the possibility to automate the verification processes with scalable, data-driven solutions. Machine learning is an effective system in the extraction of patterns within huge simulation logs, coverage databases and regression histories that go unexploited during traditional processing. Early applications such as intelligent test generation with reinforcement learning, automated debug assisting with failure clustering and waveform pattern recognition, coverage optimization with predictive analytics and natural language processing with verification planning have shown verifiable improvements in verification efficiency. The combination of advanced machine learning algorithms, convenient compute infrastructure in the cloud and the availability of extensive verification data sets places AI as a real leverage of modern verification systems as an extension layer. Nevertheless, in order to achieve full potential, it is necessary to overcome issues associated with the quality of data, interpretability of the models, integration with the existing UVM and formal verification flows and cross-design generalization. The trend is to AI-native verification ecosystems where engineers are no longer engaged in manual test development but only in managing intelligent automation processes.
Data Sovereignty and Compliance in Multi-Cloud Deployments: Evaluating Governance Models and Regulatory Challenges
The rapid adoption of multi-cloud strategies has driven a sea-change in enterprise architecture, providing unprecedented scalability, operational resilience, and freedom from vendor lock-in while creating a new set of governance issues such as how to address compliance and data sovereignty across diverse cloud environments. As many organizations utilize multiple CSPs located in different jurisdictions, there are differences in data protection standards, data localization requirements, and privacy regulations between jurisdictions that lead to a fragmented global regulatory landscape in cloud computing. This article reviews the intersection of data sovereignty principles and regulatory compliance in a cross-cloud, multi-cloud, shared distributed cloud environment. It covers the key elements of governance, technical controls, and regulatory complexities, and distinguishes between key concepts such as data sovereignty, data residency, and jurisdictional compliance, as well as distributed cloud challenges. This article reviews possible governance frameworks and technical implementations and gives a practical framework of how organizations can comply with data sovereignty and still retain operational flexibility.
Design Patterns for Integrating AI-driven Automation in Multi-Cloud CRM/ERP Ecosystems
Enterprise organizations increasingly depend on multi-cloud strategies to host mission-critical customer relationship management (CRM) and enterprise resource planning (ERP) systems. At the same time, artificial intelligence (AI) is becoming deeply embedded in business operations through automation, decision support, and predictive intelligence. Integrating AI-driven automation across distributed, heterogeneous cloud environments introduces architectural, operational, and governance complexities that exceed the capabilities of traditional integration approaches. This article presents a structured set of reusable design patterns for implementing AI-powered automation in multi-cloud CRM and ERP ecosystems. The patterns are derived from real-world enterprise modernization initiatives, including a large-scale healthcare transformation program spanning multiple major cloud platforms. Five core patterns are proposed: (1) Event-Driven Orchestration Layer for decoupled workflow coordination, (2) Intelligent API Gateway for adaptive service routing and resilience, (3) Composable Microservices with Embedded AI Agents for modular automation, (4) Unified Data Fabric with AI Data Pipelines for cross-cloud data consistency and governance, and (5) Adaptive Feedback Loop Automation for continuous learning and system optimization. Together, these patterns address recurring challenges related to orchestration complexity, data fragmentation, regulatory compliance, scalability, and operational resilience. Implementation considerations are examined across technical, organizational, and governance dimensions. A healthcare case context illustrates practical application, demonstrating measurable improvements in processing efficiency, operational effectiveness, and regulatory compliance. The paper contributes an applied architectural framework that enables enterprises to design resilient, governable, and scalable AI automation platforms aligned with long-term digital transformation objectives.
Agent-Driven Orchestration for RESTful Enterprise APIs
Orchestration mechanisms in distributed enterprise systems must be adaptable to runtime variability and governed by compliance; however, static rule-based REST orchestration cannot handle transient failures, nondeterministic eligibility outcomes, and changing policies. This section describes the formal agent-based orchestration model that extends deterministic REST workflows by integrating controlled decision autonomy. The model has four agent classes: eligibility agents, routing agents, failure-handling agents, and governance agents, implemented as independent, stateless services. Evaluation in realistic and representative enterprise settings measures operational gains from context-aware decision support. These insights, adopted in smart traffic management systems, yield important reductions in processing inefficiencies. The results imply that controlled autonomy can be feasible for REST-based enterprise systems without forsaking the compliance, auditability, and HTTP semantics afforded by centralized authorization mechanisms. This article's proposed architecture thus seeks to provide organizations a balance between operational efficiency and architectural flexibility for data-intensive systems.
Event-Driven Document Processing: MongoDB, Flink, and AI Schema Evolution
Document-based data systems have changed how organizations handle data by allowing flexible structures without fixed schemas, however processing large volumes of documents presents challenges where schemas change unpredictably, document structures vary widely, and relationships between data elements shift over time. This paper proposes a conceptual framework that combines MongoDB as an event store, Apache Flink for stream processing on Kubernetes, and AI-powered schema management, where the framework envisions MongoDB storing documents in sharded clusters that handle high read and write volumes. As documents change by adding new fields, changing nesting depth, or shifting meaning, traditional pipelines fail to maintain consistency, therefore the proposed system includes an AI layer designed to learn document structures, identify relationships, and detect changes automatically. The design incorporates large language models to process unstructured data and extract entities, while embedding models would match new fields to known patterns, and Apache Flink on Kubernetes would process document changes in real time, normalizing and enriching data as it flows. The system integrates with lakehouse platforms through filtering and metadata optimization. This paper presents a conceptual architectural framework with proposed design patterns based entirely on publicly available research, open-source technologies, and theoretical analysis, where no proprietary data, production systems, or organizational deployments are referenced. Empirical validation through benchmark testing using publicly available datasets including TPC-H benchmark data, Yelp Open Dataset, GitHub Archive, Intel Berkeley Research Lab sensor data, and synthetic healthcare data from Synthea, along with AI model accuracy evaluation and simulated deployment metrics are identified as critical future work. Specific performance improvements, cost-benefit analyses, and model selection criteria require experimental validation before production deployment using these public domain datasets. The framework provides detailed implementation guidance including model selection criteria, training methodologies, cost-latency trade-offs, and comprehensive experimental design for validation using publicly accessible benchmark datasets to ensure complete independence from any specific organization or employer, maintaining transparency and reproducibility through exclusive reliance on open-source tools and public data sources. This paper presents a conceptual architectural framework with proposed design patterns. Empirical validation through benchmark testing, AI model accuracy evaluation, and production deployment metrics are identified as critical future work. Specific performance improvements, cost-benefit analyses, and model selection criteria require experimental validation before production deployment. The framework provides detailed implementation guidance, including model selection criteria, training methodologies, cost-latency trade-offs, and a comprehensive experimental design for validation.
The Performance Paradox of Indian Large, Mid and Small Cap Funds
This paper evaluates managerial skill across Indian Large, Mid, and Small Cap mutual funds using risk-adjusted metrics and the Carhart Four-Factor model. Findings indicate that most outperformance is driven by systematic Size and Value premiums rather than idiosyncratic alpha. Motilal Oswal Midcap was the only fund to demonstrate statistically significant managerial skill, while others relied on market momentum. Results suggest the Momentum factor adds little explanatory power, making the Fama-French Three-Factor model sufficient for this period. The study concludes that Mid-Cap funds offer superior efficiency, though structural characteristics are better predictors of success than past returns.
Hybrid Deep Learning Framework for Efficient Aerial Image Classification using Aid Dataset
Aerial image classification is a crucial task in remote sensing applications like land use analysis, urban planning, and environmental studies. However, the task of achieving high classification accuracy with efficient computation is still a challenging problem because of the complex variations and high intra-class diversity in aerial images. This paper proposes a hybrid deep learning approach for efficient aerial image classification on the Aerial Image Dataset (AID). To begin with, a set of intensive preprocessing steps such as image resizing, normalization, and data augmentation are performed to improve the quality of the data and enhance generalization. Robust feature extraction is further carried out using a feature fusion approach that combines deep features from three different convolutional neural networks: ResNet50, VGG16, and InceptionV3, to extract complementary information from aerial images. The fused feature vectors are then classified using ensemble learning methods, namely bagging and boosting, to enhance the robustness of classification and prevent overfitting. The proposed framework is tested on the AID dataset using accuracy, precision, recall, F1-measure, and confusion matrix analysis. The experimental results show that the hybrid deep learning method performs significantly better than the individual deep models in terms of classification accuracy and robustness on various aerial scene categories. The results validate the effectiveness of the proposed deep feature fusion and ensemble learning method for scalable and accurate aerial image classification.
Autism Spectrum Disorder Detection Using Case-Based Reasoning: A Clinical Decision Support Framework
Early detection of Autism Spectrum Disorder (ASD) remains critical for improving developmental outcomes, yet traditional diagnostic approaches face challenges of subjectivity, time-intensive assessments, and limited access to specialist expertise. This article presents a comprehensive Case-Based Reasoning (CBR) framework for ASD detection that imitates clinician expertise by retrieving and adapting solutions from historical cases. Our system structures patient data, incorporating developmental history, behavioral symptoms, and standardized screening scores into a reusable case base. The framework implements a CBR cycle: case representation using description logic, similarity-based retrieval via weighted Euclidean distance, rule-enhanced adaptation, and continuous case base expansion through validated retention policies. The system provides explainable recommendations by referencing specific historical cases, enabling clinicians to understand and validate diagnostic suggestions. This study proposes a practical and transparent AI-assisted autism screening approach that complements clinical expertise.
Enhancing Indonesian civil servants' professionalism through integrated frameworks
The Indonesian Civil Service Professionalism Index (IP ASN) is frequently regarded as a compliance tool for personnel administration rather than a catalyst for organizational development. This study develops and validates an IP ASN enhancement model specifically for the Secretariat General of the House of Representatives (Setjen DPR), a high-level political bureaucracy, by incorporating the four dimensions of the index into a quantifiable, equitable, and adaptable capability framework. We use a mixed-methods sequential explanatory design that includes member checking, in-depth interviews with senior officials at Setjen DPR, document/performance reviews, and multi-stakeholder focus groups with major internal units. The results underscore the existence of value chains that are fundamentally flawed due to the degree-level emphasis on relevance, the inadequate implementation of the 70:20:10 learning model, the absence of structured performance dialogues associated with compensation, and the enforcement of discipline without ethical pathways. The proposed architecture is designed to be implementable by incorporating standardized requirements, mandatory training assessments, performance indicators, ethics committees, robust governance, and data integration. The model is policy-fit and technology-ready, transforming IP ASN from a compliance checklist to a sustainable, auditable performance enabler.
Volatility Spillover and Dynamic Connectedness Between US and Indian Stock Markets: Evidence from S&P 500 and NIFTY 50 (2023–2025)
Introduction: Understanding volatility transmission between developed and emerging equity markets is essential for investors, policymakers, and researchers. The interdependence between the US and Indian stock markets has increased due to globalization, capital mobility, and financial integration. This study examines volatility dynamics and spillover effects between the S&P 500 and NIFTY 50 indices using recent daily data. It contributes to the literature by analyzing persistence, asymmetric volatility, and dynamic correlations to assess diversification potential and financial market linkages. Objectives: To examine volatility persistence and clustering in the S&P 500 and NIFTY 50 indices. To analyze asymmetric volatility effects using EGARCH models. To evaluate volatility spillover and transmission mechanisms between the two markets. To assess dynamic correlations and diversification implications. Methods: Daily closing price data from January 1, 2023, to December 31, 2025 (1,513 observations) were analyzed. Stationarity and normality were tested using standard statistical tests. Vector Autoregression (VAR) was applied to examine interdependence. Volatility dynamics were estimated using GARCH (1,1) and EGARCH models to capture persistence and leverage effects. Forecast Error Variance Decomposition (FEVD) and Granger causality tests were used to identify spillover direction. Dynamic Conditional Correlation (DCC) and rolling correlation analysis were conducted to assess time-varying relationships. Results: Results show significant non-normality with fat-tailed distributions. Volatility persistence is higher in NIFTY 50 (α+β=1.072) compared to S&P 500 (α+β=0.534), indicating slower mean reversion. EGARCH results confirm significant leverage effects in both markets, where negative shocks increase volatility more than positive shocks. FEVD reveals unidirectional volatility spillover from the S&P 500 to NIFTY 50, with US shocks explaining 14.3% of Indian market variance. Dynamic correlations remain low on average but increase during periods of market stress. Granger causality results indicate weak return-level linkages but significant volatility transmission. Overall, diversification benefits exist during stable periods but weaken during crises. Conclusions: The study confirms significant volatility persistence, asymmetric effects, and unidirectional spillover from the S&P 500 to NIFTY 50. While diversification benefits exist during stable periods, increased correlations during crises reduce diversification effectiveness, highlighting stronger global market integration and risk transmission.
Electric Vehicle Business: How Entrepreneurs are Involved in Decision-Making in Business Development and Sustainability
Text-based data increasingly poses methodological challenges, requiring methods to manage, visualize, analyze, and interpret it. This paper aims to propose analytics as a possible solution to these challenges, while also demonstrating its potential for innovation, intentionality and decision making in the context of business development and sustainability. This paper uses qualitative methods to describe general application areas (contexts) and presents specific use cases, analysis, findings, and discussions for each application area. Research results that demonstrate pragmatic tools for analyzing and visualizing related business impacts in terms of innovation, intention, decision making, business development, and sustainability. This analysis focuses only on related aspects. Future research could expand on other impacts. The results of this study demonstrate that pragmatic tools for analyzing and visualizing business impacts are expected to provide insights for entrepreneurs, organizations, and governments. This paper contributes to this methodology by providing an overview of qualitative approaches focused on innovation, intention, decision-making, business development, and sustainability.
Human Resource Management Strategy Through Leadership Communication and Employee Involvement in the Institution Itb Higher Education Haji Agus Salim
This study aims to analyze the influence of leadership communication and employee involvement on work productivity through the mediation of human resource management (HR) strategies at ITB Haji Agus Salim Bukittinggi. In the context of global competition and digital transformation, higher education institutions are required to improve organizational performance through effective human resource governance. The quantitative approach is used with explanatory research methods and pathway analysis techniques to test the relationships between variables. The research population included lecturers and education staff with sampling using the Slovin formula. The results of the study show that leadership communication has a significant positive effect on human resource management and employee productivity. On the other hand, employee involvement has a negative effect on HR management, but it still has a positive impact on productivity. HR management strategies have been shown to have a partial mediating role in the relationship between leadership communication and employee engagement on productivity. The implications of this study emphasize the importance of integration between leadership behavior, employee engagement, and HR management systems in creating a productive and competitive work environment in the higher education sector.
Fine-tuning versus Prompting: Choosing the Right Approach for Large Language Model Applications
Large language models offer organizations flexibility: they can either tune the weights of the model on their domain data or they can design their prompts to elicit certain behavior from their domain-agnostic models. This article describes a framework for deciding between the tradeoffs, costs, and logistics of weight tuning and prompt design. This discussion highlights that neither approach is superior, and the best decision depends on task complexity, data availability, latency requirements, how much maintenance the system can afford, and the organization. Solutions with fine-tuning give more specialized behavior, but require curated data, compute resources, and monitoring to maintain the model's performance. Prompting is useful for fast model iteration and maintains model generality, but may not be useful in high-complexity applications where system domain knowledge is needed to remain stable. The article discusses systematic evaluation and measurement both for guiding adaptation decisions and for benchmarking, A/B testing, and monitoring. It also highlights the models specifically trained for reasoning, many of which are now defaulting to (extended) chain-of-thought processing. This has implications for whether prompting or fine-tuning methods are more appropriate, depending on the reasoning ability desired. In this way, the framework provides a systematic approach to identifying and employing the appropriate adaptation methods according to the performance and efficiency goals for applications built using large language models.
Enhancing Healthcare Data Integrity Through Blockchain-Based Master Data Management: An Architectural Framework
This technical article explores an innovative approach to Master Data Management (MDM) using blockchain technology, addressing the specific challenges faced by healthcare organizations. It presents a comprehensive solution that combines blockchain with attribute-based encryption, IPFS (Inter Planetary File System), and CP-ABE (Ciphertext-Policy Attribute-Based Encryption) technologies to create a secure, decentralized, and tamper-proof data management system. The article examines current challenges in healthcare data management, including data silos, integration issues, and security vulnerabilities, while proposing a multi-layered architectural solution. The implementation strategy outlines a phased approach, covering infrastructure setup, data migration, and operational monitoring. The article also addresses critical security considerations through blockchain immutability, advanced encryption frameworks, and automated threat detection systems. Furthermore, the article discusses future considerations focusing on scalability and interoperability challenges in healthcare blockchain implementations, providing insights into potential developments and improvements in the field.
Edge AI for Autonomous Device Security Management
For industrial, healthcare, consumer and enterprise environments, the number of connected devices has fundamentally expanded the attack surface of the modern enterprise beyond the reach of centralized security architectures. Resource constrained endpoints are generally connected intermittently via diverse communication protocols․ What is needed is not simply another security model: it need autonomous real-time enforcement solutions that do not rely on centralized infrastructure․ Edge Artificial Intelligence enables solutions to the structural imbalance by incorporating clever threat detection and response directly into the distributed device ecosystem․ The hierarchical deployment model supports on-device inference, gateway-level data aggregation, and federated learning, thus preserving data privacy while minimizing the communication burden over heterogeneous deployment environments․ Flexible machine learning models, including decision tree classifiers, temporal sequence models, and compressed neural network architectures, support accurate low-latency threat detection on devices with limited resources․ The system implementation comprises four layers: threat detection, access control, vulnerability management and automated incident response, forming a closed-loop enforcement system, which does not rely on a central controller․ The challenges posed by adversarial machine learning, model drift, regulatory compliance with law, and model explainability are addressed using adversarial training, federated learning, and interpretable model explanation frameworks․ The security architecture is a self-adaptive edge AI fabric, which is suitable for the perpetually-evolving distributed environment․
Regulatory-Grade AI Change Management: Versioning, Traceability, and Rollback for Iterative Algorithms in Medical Devices
Managing the change management process around AI-enabled software functions embedded within other medical device products requires more than deterministic software governance frameworks. Changing how businesses manage AI in medical devices needs more than just strict rules. Regular updates to an AI model—like changes to the training data, the way the model is built, its settings, or how it processes results—can create different and unexpected risks that might not be clear from overall performance numbers. So, the rules for managing changes should involve keeping a detailed record of all changes made, creating processes that can be repeated and checked with secure signed documents, and using clear step-by-step checks with set plans for what changes are allowed. Additionally, continuous monitoring at each deployment stage, ongoing model health surveillance, and clearly defined quantitative conditions for reverting changes are essential. Organizations must also establish structured rollback procedures with traceable documentation and require formal performance testing and approval of the model both before and after any changes or rollbacks are executed. These controls should be completely included in the standard operating procedures of the quality management system to make sure that AI-enabled software works clearly, can be checked, and stays in line with the necessary rules during the entire product lifecycle.
Zero Trust Architecture for Generative AI: Securing Prompts, Retrieval, and Agent Tool-Use in Regulated Environments
The deployment of generative AI systems in controlled contexts presents an entirely enlarged attack surface: The use of prompts, retrieval pipelines and agentic tool invocations are all unique and consequential authorization events that cannot be regulated by perimeter security controls. Zero Trust Architecture offers the conceptual basis needed to overcome these issues with a requirement to do policy review on a per-request basis based on verified identity, device posture, and real-time risk indicators as opposed to implied session trust. An architecture that can be defended as ZT-GenAI necessitates coordinated enforcement across five interdependent control layers: Prompt Envelope integrity validation, Attribute-Based Access Control governed retrieval, token-level Redaction Gates, Tool Broker intermediation with scoped token issuance, and workflows of supervisory approval of high-impact agentic actions. Such an architecture can be evaluated through injection resilience testing, policy effectiveness testing, audit completeness testing, operational latency sensitivity testing, and so on, and the evidence they produce can be the basis of regulatory defensibility and model risk governance. The architectural designs shown herein provide a repeatable template for implementing generative AI capabilities into sensitive operational settings without any authorization rigor or auditability sacrifices.
Explainable Conversations: Enabling Transparency in Large Language Model Responses
Conversational AI systems powered by large language models increasingly handle high-stakes enterprise tasks, yet their reasoning processes remain opaque to users. This opacity creates barriers to trust, limits adoption in regulated industries, and complicates compliance auditing. We introduce the Explainable Conversations Framework (X-LLM), a three-layer architectural approach that embeds transparency throughout conversational AI systems rather than treating explainability as an afterthought. X-LLM integrates model-level mechanisms (citation frameworks, reasoning traces, confidence calibration), interaction-level design patterns (progressive disclosure interfaces, adaptive explanation depth), and system-level infrastructure (audit logging, governance controls, evaluation harnesses). We formalize the Cognitive Transparency Index (CTI), a composite metric combining factual traceability, reasoning clarity, and user interpretability into a unified transparency assessment.Through a validation study using demonstration data from the AgentArch benchmark [24], we demonstrate how X-LLM principles guide practical implementation decisions and improve system trustworthiness. We position our framework against existing explainability approaches and RAG architectures, identifying where X-LLM provides novel contributions and where it synthesizes established patterns. The framework offers a structured methodology for organizations building conversational AI systems that must balance sophisticated capabilities with regulatory requirements and user comprehension needs.
Mapping Wind Erosion Susceptibility and Land Degradation Priority Areas Using GIS and Remote Sensing in Semi-Arid Northeastern Algeria
Wind erosion is an important factor in land degradation in arid and semi-arid areas, especially in regions with high wind speed, weak soil cohesion, and limited vegetation cover. This research evaluated the susceptibility of wind erosion in the Nemamcha area (Southern part of the Province of Khenchela in northeastern Algeria) using a combination of remote sensing data, climate parameters, soil data, topographic factors, and human-induced factors in a GIS platform based on the weighted multi-attribute approach. Vegetation protection was considered using the Normalized Difference Vegetation Index obtained from Landsat-8 satellite images, while soil and soil surface characteristics were accounted for using land cover types, soil type and classification, soil texture, grain size index, soil moisture index, geology, slope elevation, wind speed values, human pressure, and road network density. Lastly, the map of susceptibility was classified into five susceptibility levels (very low to very high). It was revealed that the high and very high levels of susceptibility cover approximately 58.29% and 24.62% of the area of interest, while the medium susceptibility level is around 11.12%, and the areas classified as low and very low only account for 5.97%. It was noted that the areas of high susceptibility generally correspond to sandy soils and sparse vegetation cover.
Adaptive Reliability Engineering for Transaction-Intensive Enterprise Platforms
Reliability engineering for transaction-intensive distributed platforms has evolved beyond static provisioning and threshold-based fault tolerance. Modern cloud-native systems operate under nonlinear workload volatility, metastable degradation risks, and complex dependency-induced failure propagation, rendering traditional reliability models insufficient. This paper formalizes Adaptive Reliability Engineering (ARE) as a control-theoretic framework that transforms reliability from static configuration into a closed-loop operational discipline. By integrating real-time telemetry, dynamic load shedding, health-aware routing, circuit breaker isolation, and feedback-driven resource governance, ARE enables continuous system stabilization under volatile demand conditions. The framework addresses metastable failure amplification, retry-induced cascading collapse, and inefficiencies in error-path execution by introducing adaptive control surfaces that dynamically regulate resource allocation and service-level objective (SLO) compliance. Unlike machine learning–dependent resource managers that require prolonged training cycles and exploration overhead, ARE emphasizes deterministic feedback control mechanisms capable of immediate responsiveness without extended data collection phases. The proposed framework generalizes across financial transaction infrastructures, digital commerce platforms, and cloud-native microservices architectures.
A Three-Body Model for AI-Driven Software Development: Integrating Business, Engineering, and Large Language Models
The integration of Large Language Models into software development has shown remarkable potential, yet existing approaches remain fragmented across isolated development stages without addressing the critical need for organizational alignment and contextual awareness. This article proposes a three-body model that establishes continuous collaboration between Business Groups, Software Engineers, and enterprise-tuned Large Language Models within a unified framework. Unlike conventional approaches treating AI as standalone productivity tools, this model positions the LLM as an intelligent intermediary trained specifically on organizational theming standards, architectural patterns, and branding guidelines. Business stakeholders articulate requirements through natural language interfaces, the context-aware LLM translates these inputs into technically actionable artifacts aligned with corporate standards, and engineers validate and refine outputs while providing feedback for continuous improvement. This triadic structure creates a closed-loop system that maintains semantic integrity across business-technical boundaries while embedding organizational intelligence directly into development workflows. Empirical evaluation demonstrates meaningful improvements in productivity, design consistency, automation coverage, and stakeholder alignment compared to traditional agile methodologies. The article redefines engineering roles from primary creators to AI orchestrators responsible for curation, validation, and strategic oversight. This article contributes a scalable, context-aware paradigm that balances automation efficiency with human judgment, establishing foundational principles for the next generation of enterprise software development in the artificial intelligence era.
Autonomous AI Agents in Enterprise CRM: Architecture, Governance, and Operational Safety
Enterprise Customer Relationship Management platforms are undergoing a fundamental transformation from passive systems of record into intelligent systems capable of autonomous decision execution across sales, service, and marketing functions. Deterministic rule engines and scripted workflow automation, which have historically formed the backbone of CRM operational efficiency, exhibit structural limitations when confronted with the contextually complex, multi-signal decision environments characteristic of large-scale B2B enterprises. The emergence of large language model-based agentic frameworks has introduced a qualitatively different automation paradigm, enabling systems to reason over unstructured inputs, decompose complex objectives into executable action sequences, and adapt dynamically to intermediate outcomes in ways that rule-based predecessors cannot replicate. However, deploying such agents within mission-critical enterprise environments introduces significant challenges related to governance, explainability, regulatory compliance, and operational safety that existing agent frameworks do not adequately address. This article proposes a comprehensive reference architecture for integrating autonomous AI agents within enterprise CRM platforms, organized across four interdependent layers encompassing agent orchestration, policy enforcement, human-in-the-loop oversight, and auditable execution substrate. A bounded autonomy model is introduced to calibrate agent operational latitude through dynamic trust scoring and risk-based escalation mechanisms, enabling organizations to balance autonomous efficiency with institutional accountability. Governance components, including explainability logging, prompt versioning, and compliance-aware data access controls, are embedded as foundational design constraints to satisfy regulatory obligations common in regulated industry environments. A production deployment within a global B2B enterprise environment validates the architectural framework, demonstrating measurable improvements in lead qualification cycle time, opportunity win rates, customer communication response rates and operational cost efficiency while maintaining full compliance with applicable data protection requirements. Hybrid human-agent workflow benchmarking confirms substantial efficiency gains compared to traditional rule-based automation without compromising human control or auditability. The architectural principles introduced in this article are generalizable across large-scale SaaS platforms beyond the Salesforce implementation context, providing a replicable blueprint for trustworthy autonomous AI adoption in mission-critical enterprise business systems.
Energy-Efficient Semiconductor Physical Design and Its Role in Sustainable Computing Systems
With environmental, economic, and social considerations dominating computer system design, energy efficiency has become one of the primary concerns. This article will present the basic theory behind energy-efficient semiconductor physical design and discuss its implications for energy-sustainable computer systems. Topics include static and dynamic power dissipation in IC circuits, technology scaling, and power analysis methodology. For power optimization, the following physical design methodology and techniques are discussed: multi-voltage domain design, clock tree synthesis, and clever placement and routing techniques. The article reviews state-of-the-art power management techniques such as dynamic voltage and frequency scaling, power gating, and near-threshold operation and their impact on energy usage and sustainability, as well as environmental, economic, and policy aspects of sustainability. While carbon-aware design optimization and lifecycle assessment strategies are covered, energy-efficient physical design techniques continue to be a critical aspect of responsible technology development. Along with technological advancement and economic drivers, regulation in an increasingly power-constrained computing landscape has elevated the importance of energy-efficient physical design techniques.
High-Precision Load Balancing: Solving Tail Latency via State-Aware Probing and Split-Task Methodology
One of the biggest challenges with hyper-scale distributed systems is routing requests across thousands of microservices. Customary load-balancing approaches are often a bottleneck for ultra-low tail latency systems because of caches holding stale metadata, such as routes, which can be a problem in high-QPS systems. The server state is propagated with a time lag with respect to the load signal, leading to information asymmetry bottlenecks and hotspots, i.e, delays in the server responsiveness. State-aware probing measures these effects by accurately tracking the most recent states of the server task internals using synchronous piggybacked telemetry and asynchronous probe intervals. This probing uses queue depth, CPU, and memory pressure to create cluster maps to route requests. The current capacity (not historic averages) of these nodes is then used to route a request to the appropriate nodes that can process it. Recursive load-balancing policies using split-task experimental designs, which isolate and measure each variable with extreme precision, change the nature of load balancing from static algorithmic spreading to dynamic information-rich orchestration. This combination greatly increases infrastructure efficiency, simplifying the debugging and optimization of critical workloads such as machine learning inference and real-time search indexing.
Analyzing Technology Access Inequality in California’s Homeless Population Using Machine Learning Techniques
The research analyzes technology access disparity on a county level throughout California through an interpretable machine-learning framework uniting supervised classification, explainable AI, and unsupervised clustering. With publicly available broadband and socioeconomic variables of the dataset Older 58 counties in California, using a dichotomous outcome model of lower and higher-access counties and evaluating several algorithms (logistic regression, SVM with RBF kernel, random forest, gradient boosting, and extra trees), the paper uses a dataset of publicly available broadband and socioeconomic variables entitled U.S. Broadband Availability. Results of comparative analysis indicate that logistic regression is the most effective one in general (accuracy about 0.611, F1-score of about 0.462), which also has a moderate discriminative ability (ROC-AUC about 0.59), with the best measure of precision-recall (average precision about 0.66). Reliability is also evaluated in this study by calculation and threshold-sensitivity analysis to bring out trade-offs pertinent to policy targeting. Explainability analyses (feature importance, SHAP, and partial dependence) reveal that structural socioeconomic aspects, particularly poverty, unemployment, population dynamics, and social assistance dependency, are core contributors to broadband exclusion, whereas direct household-level technology indicators have a secondary role. Last, the distinct profiles of the K-means clustering (K = 3) clearly distinguish broadband-access profiles (well-connected, transitional, and severely underserved), which then inform more customized intervention strategies.
Modern Data Store Selection: A Quantitative Framework for Enterprise Architecture
The contemporary enterprise architecture is experiencing challenges in the selection of data stores as the global data is growing exponentially, and the organizations are keeping high availability needs. The economic impact of storage infrastructure failures has grown to be very high, and of the many enterprises, most have incurred expensive downtimes which directly affect not only revenue but also customer satisfaction. Storage technology choices entail basic trade-offs in architecture between row-based and column-based forms of execution, where columnar systems have proven significantly superior to analytical workloads through late materialization, block iteration optimization and invisible join methods that scale up to provide exponential performance benefit. Distributed systems have to compromise consistency assurances and latency needs because even slight, measurably, decreases in response time have a negative impact on user interactions. The performance characteristics of the various categories of technologies are radically different, with relational and document databases displaying different performance with respect to connection loads as well as workload constituencies and memory resident caching systems demonstrating less than millisecond latencies with extraordinary throughput rates. At service level, the goals are converted into measurable error budgets limiting acceptable downtime levels, compelling architects to consider storage technologies in a worst-case recovery scenario instead of in an ideal performance. Modern business ventures are more prone to implementing polyglot persistence models where different special purpose data stores are used depending on the workload properties. Nonetheless, the pattern of architecture adds significant complexity to integration, with the organizations organizing data flow with many systems operating with high frequency rates, and reliability of pipelines and synchronization are the main elements of operation instead of the peculiarities of individual database performance.
Restoring Machine-Readability in the Age of AI-Synthesized Answers
AI-based information retrieval systems have transformed content interaction patterns. Users increasingly expect answers in the form of synthesized responses, rather than interface navigation. Modern web architectures often rely on client-side rendering, which typically serves content by running JavaScript code and is difficult for automated agents to crawl, as JavaScript-infused pages appear as empty HTML shells to conventional web crawlers. This limits agent access to material until execution occurs. This poses concerns for technical documentation, educational materials, and knowledge repositories where complete and verifiable sources cannot be provided and alternative sources or incomplete content must be used. Dual-delivery architectures recognize requesting agent capabilities to deliver pre-rendered static HTML to automated browsers while delivering interactive content to human visitors. Three general categories exist for implementing dual-delivery architectures: network edge architectures, server-side rendered architectures, and hybrid architectures that deliver content using a single codebase in an accessible manner. True success relies on content equivalence between machine and human views, improved semantic markup and structured metadata vocabularies, and measurement frameworks through user-agent analysis and accessibility testing. The verifiable engineering practice of machine-readability and machine-visibility improvements can become the new standard of successful creative practice.
Distributed Intelligence Fabric: A Framework for Real-Time Human–AI Collaboration
This article presents Distributed Intelligence Fabric as a conceptual architectural framework that proposes seamless collaboration between humans, AI agents, and distributed compute resources across edge, cloud, and client environments. The DIF model addresses fundamental limitations in contemporary distributed AI systems through the theoretical introduction of Temporal Context Graphs for cross-system intelligence sharing, Cognitive Routing algorithms for dynamic task delegation, and Predictive Decision Pipelines for proactive computation strategies. The article examines key technical components including autonomous routing models and contextual inference engines that would support real-time collaboration and decision synchronization. Conceptual implementation scenarios across electric vehicle fleet management, AdTech bidding systems, enterprise workflows, fraud monitoring, and IoT sensor networks demonstrate the theoretical applicability of DIF architecture. The article explores comprehensive safety mechanisms, governance frameworks, and ethical considerations essential for distributed agent systems while identifying future directions for intelligent system architectures and enterprise AI governance requirements. The proposed framework requires substantial empirical validation and prototype development to establish practical feasibility and performance characteristics.
Enterprise Data Migration: Strategic Imperatives and Execution Excellence
Enterprise data migration has become both a strategic necessity and a technical implementation project that directly influences competitive positioning and organizational resilience. A shift to cloud infrastructure is a conceptual shift in the design of data platforms and analytics infrastructure applied by organizations. However, the execution track record reveals substantial challenges. The majority of migration initiatives fail to achieve their stated objectives due to inadequate visibility into data characteristics, incomplete understanding of system dependencies, and undetected quality issues that multiply as system complexity increases. The financial consequences of migration inefficiencies extend beyond individual organizations. Industry-wide impacts include billions of dollars in wasted expenditure and eroded shareholder value. For enterprises operating in regulated sectors such as financial services and retail, migration excellence becomes existentially important rather than merely operationally desirable. Data accuracy directly affects revenue recognition and compliance reporting in these industries. Successful migration execution requires establishing repeatable patterns through standardized landing zones, idempotent transformations, comprehensive reconciliation capabilities, and robust observability mechanisms. The economic value proposition for well-executed modernization programs demonstrates substantial returns on investment with rapid payback periods. Platform adoption patterns reveal ongoing consolidation toward elastic, governed architectures. Governance structures, which serve as functioning capabilities with automatic enforcement, and audit-ready artifacts embedded in designs of migration suites, should replace documentation exercises. The infrastructure landscape supporting these transformations continues expanding globally to meet latency, residency, and continuity requirements essential for enterprise-scale deployments.
Enterprise Cloud Migration at Scale: Frameworks and Practices for Legacy-to-GCP Transformation
Enterprise migration to Google Cloud Platform represents a multidimensional transformation that extends beyond technical infrastructure to encompass architecture redesign, operational model changes, governance restructuring, and organizational culture shifts. This paper synthesizes lessons learned from large-scale legacy-to-GCP migrations and examines critical success factors across six domains: strategic planning, execution patterns, security integration, observability practices, cost optimization, and collaboration models. The study addresses three guiding questions. First, how can organizations sequence migration activities to maximize business value realization? Second, what execution patterns minimize disruption while ensuring data consistency? Third, how should security, observability, and cost governance be embedded as foundational capabilities rather than retrofitted controls? Drawing on practitioner experience and established frameworks from Google Cloud Architecture documentation, industry analyses, and enterprise transformation research, this paper identifies effective patterns alongside common anti-patterns. Strategic planning requires value-first domain sequencing and rigorous total cost of ownership modeling. Execution demands coordinated bulk data transfer with streaming change data capture, validated through shadow reads and progressive traffic shifting. Security must follow zero-trust principles with automated compliance evidence generation. Observability infrastructure should precede scale growth, incorporating standardized dashboards and incident response procedures. Cost optimization relies on FinOps disciplines combined with architectural choices such as BigQuery partitioning and query tuning. Finally, product-aligned teams and platform guilds reduce organizational friction while maintaining governance standards. The findings highlight that organizations treating migration as purely technical infrastructure projects often encounter costly failures. Success requires integrated attention to technical, operational, and organizational dimensions throughout the transformation lifecycle.
Best Practices for Enterprise System Integration in Modern Organizations
Enterprise system integration represents a critical capability for modern organizations seeking to maintain operational efficiency and competitive advantage in increasingly complex technological landscapes. This comprehensive guide examines the multifaceted challenges and strategic approaches required for successful integration of diverse business applications, legacy systems, and emerging Internet of Things data streams. The article addresses fundamental integration challenges, including data standardization, schema evolution, security vulnerabilities, and the transition from batch-oriented processing to real-time analytics capabilities. Through articles on contemporary integration architectures such as data lakehouses, the work demonstrates how organizations can balance the scalability requirements of modern data volumes with the transactional consistency demanded by enterprise operations. The guide emphasizes the necessity of thorough strategic planning, robust data governance frameworks, and comprehensive security architectures that protect integrated systems while enabling necessary information exchange. Particular attention is devoted to the integration of sensor data from Internet of Things devices with traditional enterprise resource planning systems, highlighting the technical and organizational complexities inherent in bridging operational technology with information technology domains. The article presented encompasses phased deployment strategies, rigorous testing protocols, continuous monitoring frameworks, and the implementation of advanced intrusion detection systems capable of identifying and mitigating sophisticated cyber threats. By synthesizing best practices from manufacturing, smart city deployments, and enterprise analytics implementations, this article provides actionable guidance for organizations navigating the technical, security, and governance challenges of contemporary system integration initiatives.
Embedded Compliance Architecture for Real-Time Financial Systems Modernization
Modern financial organizations migrating from legacy batch processing infrastructures to distributed real-time environments face increasingly complex regulatory compliance challenges. Conventional compliance mechanisms are inadequate when financial transactions occur instantaneously across distributed networks, leaving institutions exposed during the interval between transaction initiation and regulatory validation. Embedded compliance architecture reduces this exposure by integrating regulatory checks directly into transaction processing flows rather than executing them as post-transaction audits. Through microservice decomposition, event-driven processing, containerization, and risk-adaptive performance optimization, organizations can build compliance systems that scale with business activity while enforcing regulatory obligations in real time. Beyond risk reduction, the architecture yields measurable operational advantages, including infrastructure efficiency, environmental sustainability, and stronger consumer protection. Real-time enforcement prevents fraud and discriminatory practices before they harm consumers, reinforcing trust in digital financial services. By reducing per-transaction compliance costs, embedded compliance also enables institutions to serve previously uneconomical customer segments, advancing financial inclusion for underbanked populations. As financial platforms scale globally, embedded compliance architectures will be essential for maintaining regulatory adherence, consumer protection, and system resilience in an increasingly complex digital financial landscape.
From Automation to Autonomy: Minimizing the Productivity J-Curve in Artificial Intelligence Adoption
The productivity paradox is not unique to a single technology revolution, with the take-up of artificial intelligence having a similar pattern to that of previous information technology revolutions. Despite improvements in task-level productivity in knowledge work, macro-level productivity statistics for the economy are not increasing. There is a disconnect between the siloed growth of technology and macroeconomic growth. Organizations are also prone to replicate the fragmentation, creating "islands of automation" rather than autonomous applications that can orchestrate the execution of knowledge tasks. The productivity J-curve assumes that investment in capability development, redesign of workflows and processes, and alignment of architectures will have a negative productivity payback if the investment in the productivity gains has not returned. The length and depth of the productivity J-curve can be reduced through systematic reuse of the lessons learned in the evolution of IT (end-to-end workflow management, development of complementary organizational capabilities, and convergence of the pilots into architectural platforms). Cloud-native integration and distributed orchestration patterns help achieve this autonomy by enabling data-powered solutions that orchestrate and distribute autonomous decisions across enterprise systems to create micro-level efficiencies that lead to system-level productivity.
Streamlining Renewal Management: A Proactive Approach Using CRM Automation
Renewal management stands as a pivotal business function directly influencing revenue stability and client retention. Traditional methods often struggle with inefficiencies from manual tracking, reactive strategies, and disjointed communication, resulting in missed opportunities and diminished client satisfaction. Modern business environments necessitate sophisticated solutions leveraging technological advancements to transform renewal processes from reactive to proactive models. Customer Relationship Management automation offers a compelling solution by systematically organizing contract data, establishing automated workflows, and providing actionable insights, driving renewal success. This article examines CRM automation implementation for renewal pipeline management through a systematic review of industry reports and empirical studies published between 2018-2025. The framework encompasses automated triggers, timeline orchestration, comprehensive data integration, and workflow bifurcation. Deal lifecycle navigation involves opportunity qualification, dynamic pricing optimization, and stakeholder alignment. Contract finalization features template-driven generation, collaborative revision management, digital execution pathways, and post-execution integration. Data-driven decision support includes predictive analytics for risk assessment, performance benchmarking, value proposition optimization, and resource allocation enhancement. These capabilities transform renewal management from an administrative function to a strategic initiative contributing meaningfully to organizational growth objectives, though implementation challenges and contextual variations merit consideration.
Runtime Delegation Strategies for Controlled Autonomy in Large-Scale Decision Systems
Commercial digital platforms rely on automated decision systems such as recommendation engines, promotion selection services, and notification delivery pipelines to operate at a massive scale under strict latency and availability requirements.These systems are typically configured using static delegation policies or model-confidence thresholds that determine whether automated decisions are executed or deferred. However, such static approaches fail to account for dynamic runtime conditions that influence the safety and reliability of automated actions. This paper argues that delegation should be treated as a runtime architectural concern rather than a static configuration or organizational policy. We propose a framework in which decision systems dynamically determine whether to act autonomously, constrain their behavior, or transition to safe fallback mechanisms during execution. The framework extends beyond traditional confidence-based gating by incorporating multiple runtime signals, including impact radius, contextual volatility, recovery cost, and historical system reliability. Building on these signals, we introduce a practitioner-oriented taxonomy of delegation strategies applicable to high-throughput decision systems. The taxonomy categorizes operational patterns that enable systems to adapt their autonomy in response to changing environmental and system conditions. We illustrate the applicability of these strategies through representative scenarios drawn from large-scale commercial decision infrastructures. By framing delegation as a first-class design dimension of automated systems, this work provides practical guidance for constructing dependable, resilient, and adaptive decision platforms capable of safely increasing autonomy while maintaining operational safeguards.
Advancing At-Speed Testing through Launch-on-Extra-Shift Architecture
At-speed testing of integrated circuits presents a fundamental challenge in balancing comprehensive fault detection with power management and manufacturing yield optimization. Traditional testing methodologies force designers to compromise between test quality and power constraints, as patterns optimized for fault coverage often generate excessive switching activity that causes voltage drops and false test failures. This article explores the implementation of launch-on-extra-shift testing architecture combined with strategic power management techniques to address these competing requirements. The article examines two primary launch mechanisms for generating test transitions: launch-off-capture, which provides relaxed timing requirements but requires computationally intensive sequential pattern generation, and launch-on-extra-shift, which enables faster combinational pattern generation but introduces stringent timing constraints on scan enable signals. A sophisticated mixed-testing architecture is presented that employs pipelined scan enable structures and dynamic control capabilities through test point integration, allowing different design regions to operate under different launch mechanisms based on their specific characteristics. To mitigate the elevated switching activity inherent in launch-on-extra-shift testing, strategic clock gating techniques are implemented through a hierarchical power management infrastructure that selectively activates or deactivates circuit portions during test operations. The implementation strategy incorporates simulation-based analysis to identify power hotspots and validate design modifications through iterative refinement processes. By combining these architectural innovations with partition-based testing approaches, the methodology achieves enhanced transition fault coverage while maintaining acceptable power profiles and manufacturing economics, demonstrating that temporal and spatial separation of testing activity can preserve pattern quality while reducing instantaneous power consumption without sacrificing fault detection capabilities.
Automating Enterprise Onboarding with PowerShell and Chef: A Scalable Framework for PLM Environments
Enterprise onboarding processes in Product Lifecycle Management environments remain plagued by manual procedures, inconsistent execution, and resource-intensive operations that create substantial delays and configuration errors. Traditional onboarding workflows require extensive IT administrator involvement for Active Directory user provisioning, application role assignments, and system configurations, particularly in manufacturing and engineering organizations where specialized PLM applications like Teamcenter and UGNX demand precise role-based access controls and environment-specific configurations. This technical review presents a scalable automation framework that integrates Windows PowerShell scripting for identity and directory management with Chef-based configuration management for PLM applications, establishing an Infrastructure-as-Code paradigm that treats user provisioning and application configuration as version-controlled, repeatable processes. The framework implements a three-tier architecture comprising orchestration, execution, and validation layers that coordinate identity management operations with application configuration tasks through parallel processing, significantly reducing onboarding completion times while minimizing configuration errors. Implementation at a global aerospace manufacturing organization demonstrates substantial operational improvements, including reduced onboarding durations, decreased error rates, and enhanced compliance with enterprise security policies. The framework's extensibility enables expansion beyond initial engineering onboarding to encompass manufacturing execution systems, quality management applications, and enterprise resource planning tools, providing a foundation for comprehensive enterprise automation initiatives that modernize IT operations while respecting constraints of legacy application ecosystems.
Commodity-Based Dynamic Spine Architectures: A Cost-Performance Framework for Hyperscale Data Center Networks
Modern hyperscale data centers face mounting pressure to balance capital expenditure constraints with demands for massive scalability and high reliability. Traditional monolithic chassis-based spine architectures create vendor lock-in, limited expansion pathways, and substantial upgrade costs. Disaggregated multi-tier spine designs using commodity switches offer a transformative alternative through modular Upper Spine, Lower Spine, and Fabric Spine layers configured in CLOS topology. This architectural pattern enables horizontal capacity growth without system-wide disruptions while significantly reducing upfront hardware costs and operational complexity. The modular design constrains failure domains, accelerating maintenance cycles and supporting five-nine availability targets through enhanced redundancy strategies. Cost advantages emerge across multiple dimensions: reduced fiber and optics requirements, flexible vendor selection, and streamlined capacity planning. Performance characteristics remain competitive with proprietary solutions while providing superior economics at scale. Organizations operating large cloud networks gain particular benefit from this architectural shift, achieving better total cost of ownership without sacrificing network performance or reliability objectives.
Nexus between Economic Openness, Money Supply, Environmental Degradation and Economic Growth New Evidence from the MENA Region: A CS-ARDL Analysis
This study explores the impact of economic (economic openness, credit, money supply) and environmental indicators (CO2 emissions) on economic growth in Middle East and North Africa (MENA) countries, considering the challenges posed by climate change. Through a theoretical process of transition to a monetary policy that incorporates these issues, the study examines potential strategies of MENA countries. It analyses empirical approaches to the relationship between climate, productivity, and monetary policy. Empirical results reveal that in the MENA region, CO2 emissions do not significantly impact long-term growth, mainly due to the high dependence on extractive industries. However, this dynamic could conceal long-term challenges, particularly in economic diversification and energy transition. Moreover, private credit has a negative effect on growth, while the money supply has a significant positive impact. Economic openness spurs growth, but its effects are less pronounced. The results call for prudent credit management and tighter regulation while underscoring the importance of energy transition and diversification-orientated economic policies to ensure long-term sustainable and environmentally sound growth.
Restituting Roman Aqueduct Hydraulic Systems: A Bibliometric Analysis for Methodological Strategy Applied to the Left-Bank Aqueduct of Tiklat
Roman aqueducts represent some of the most sophisticated hydraulic infrastructures of antiquity and constitute major components of Mediterranean cultural heritage. Research on Roman aqueducts and hydraulic systems has recently evolved through methodological advances in geophysics, digital modelling, and hydraulic simulation. This study presents a bibliometric analysis of publications indexed in Scopus that focus on the restitution of Roman aqueducts and their hydraulic systems. The corpus was analysed using descriptive statistics, co-authorship mapping, keyword co-occurrence, and co-citation networks. Results show a marked increase in publication output since 2018, reflecting growing interdisciplinarity and methodological innovation. The field remains rooted in Arts and Humanities but increasingly incorporates Earth Sciences, Environmental Sciences, and Engineering. Co-authorship and co-citation analyses reveal collaborative clusters and a stable intellectual core. Despite these advances, geographical coverage is uneven and methodological standardisation remains limited. The study highlights opportunities for enhanced integration of hydraulic modelling, digital reconstruction, and socio-territorial interpretation, providing strategic guidance for future research in archaeology and Roman aqueduct restitution, it also contributes to the development of interdisciplinary protocols applicable to hydraulic archaeological systems. This bibliometric analysis provides a strategic framework for orienting ongoing and future investigations, it also guides the methodological decision-making applied on the case of the Roman aqueduct of Tiklat (Tubusuptu).
An Empirical Study on the Integration of Lean Manufacturing and Kaizen to Increase Manufacturing Productivity in Indonesia
Introduction: The phenomenon experienced by several industries is due to a decrease in customer demand, unbalanced workstations, over capacity from what is needed, and too many activities that do not add value. Objectives: This study aims to enhance process flow by reducing process waste and minimizing time in the assembly area, thereby increasing overall efficiency and productivity. Methods: To achieve this, the research adopts a combination of the Lean Manufacturing approach and the Kaizen concept. Lean Manufacturing provides a systematic framework for eliminating waste. Meanwhile, Kaizen instills a continuous improvement mindset that ensures lean systems don't remain a one-time project. Combining these two methods allows for systematic productivity improvements through Focus Group Discussions (FGD). FGD with experts are used to guide the implementation of Fishbone Analysis and FMEA because they integrate cross-functional expert judgment, enhance content validity, and generate credible consensus in identifying root causes and process failure risks. Results: The results demonstrate significant improvements in manufacturing performance. The process time was reduced by 45.2%, manpower requirements decreased by 33.3%, and daily production output increased from 50 units to 55 units, reflecting a 10% improvement. These findings highlight not only the benefits of reducing unnecessary processes and motion but also the impact on cost efficiency and workforce optimization. Moreover, the study addresses delivery delays that previously resulted from low productivity, indicating inherent limitations within the current process. Recognizing these challenges provides valuable insights for developing strategies to overcome them. In terms of broader implications, the research suggests that the integration of Lean Manufacturing and Kaizen in the Indonesian manufacturing context holds potential for scalability and wider adoption across the industry. This research has a level of originality that lies in the development and integration of a continuous productivity improvement model that combines Lean Manufacturing approaches, Kaizen Thinking, and expert-based risk and root cause analysis. Conclusions: Overall, the results of a 45.2% reduction in process time provide a scientific contribution in the form of a practical, adaptive, continuous productivity improvement model, while simultaneously expanding the understanding of how the integration of Lean Manufacturing and Kaizen can be effectively operationalized to support the long-term competitiveness of the manufacturing industry.
Tebessa, a Heritage in Search of Valorisation: Analysis of the Untapped Tourism Potential in Tebessa, Algeria
The city of Tebessa, located in eastern Algeria, possesses an exceptionally rich historical and archaeological heritage inherited from successive civilizations ranging from the Numidian period to the Islamic era. Despite this remarkable patrimonial capital, the city remains marginal within national tourism dynamics. This article examines the paradox between the abundance of heritage resources and their weak tourism valorisation. The study adopts a qualitative approach combining documentary analysis with exploratory fieldwork conducted among institutional actors, economic stakeholders, and local residents. The findings highlight several major constraints hindering the tourism development of Tebessa’s heritage, including insufficient hospitality infrastructure, the absence of integrated territorial governance, weak heritage mediation, and poor coordination among local actors. Based on this territorial diagnosis, the article proposes strategic orientations for sustainable tourism development adapted to the realities of Algerian secondary cities and grounded in the mobilisation of local resources.
Bibliometric and Experimental Insights into Soil Liquefaction under Cyclic Loading in High-Seismicity Regions (1980–2025)
Soil liquefaction induced by cyclic loading represents one of the most critical geotechnical hazards affecting infrastructure resilience during earthquakes and dynamic loading events. Over the past decades, extensive research has been conducted to understand liquefaction mechanisms, assessment methods, and mitigation strategies. However, a systematic evaluation of the intellectual structure and long-term evolution of this research field remains limited. This study presents a comprehensive bibliometric analysis of global scientific publications related to liquefaction potential under cyclic loading over the period 1980–2025. Bibliographic data were extracted from the Scopus database using predefined search criteria to ensure transparency and reproducibility. Quantitative performance analysis and science mapping techniques were applied to identify publication growth patterns, leading authors, institutional collaborations, influential journals, and emerging research themes. Results reveal a sharp increase in research output since 2015, peaking around 2025, with Earth and Planetary Sciences (41.6%) and Engineering (25.2%) as dominant disciplines. The United States emerges as the central collaboration hub, linking major producers including China, India, and European nations. Co-citation networks highlight foundational contributions from Obermeier on paleoseismic liquefaction features, Boulanger-Idriss on triggering procedures, and Moss et al. on CPT-based assessment, while keyword co-occurrence identifies three core themes: laboratory cyclic testing, seismic hazard analysis, and geological evidence. To illustrate a gap identified in the bibliometric analysis, namely the limited representation of Mediterranean natural sands, cyclic triaxial tests were performed on a natural Algerian coastal sand under Kobe-equivalent loading conditions (CSR = 0.69). Results show density-dependent pore-pressure build-up and liquefaction resistance consistent with dominant laboratory research trends identified bibliometrically. The combined bibliometric–experimental framework provides an integrated understanding of the intellectual structure, thematic evolution, and physical behaviour governing liquefaction research, highlighting future directions toward probabilistic and data-driven assessment approaches.
Mapping Islamic Residential Architecture Research: Development of the Islamic Architecture Knowledge Integration Theory (IAKIT)
Introduction: Islamic residential architecture embodies hurma, family cohesion, and stewardship through elements such as courtyards, screened openings, and qibla‑oriented layouts, yet contemporary pressures of globalisation, climate change, and digitalisation have fragmented the related scholarship and obscured how these values are translated into current housing research and practice. Objectives: This article aims to systematically map the intellectual, thematic, and geographical structure of Islamic residential architecture research, clarify how Islamic values and maqāṣid al‑sharīʿa are operationalised within this literature, and develop an integrative theoretical framework—the Islamic Architecture Knowledge Integration Theory (IAKIT)—to guide future studies, design practice, and policy. Methods: A mixed‑method sequential design was applied to 231 Scopus‑indexed publications (1982–2026), combining descriptive and inferential bibliometric techniques (trend analysis, co‑citation, bibliographic coupling, keyword co‑occurrence, cluster modularity, and correlation tests) with qualitative, abductive interpretation of emerging thematic clusters, all framed by a critical realist ontology and post‑positivist epistemology. Results: The analysis reveals sustained growth in output, a socio‑technical disciplinary profile, and four robust thematic clusters—Cultural‑Spiritual Foundations, Environmental‑Performance Integration, Methodological‑Analytical Paradigms, and Heritage‑Innovation Synthesis—that together account for a high proportion of variance and demonstrate increasing integration of sustainability, digital tools, and heritage‑based design logics across regions. These clusters show statistically significant temporal trends and strong links between regional research communities, thematic orientations, and methodological innovation. Conclusions: The findings consolidate a previously scattered body of work, substantiate IAKIT as a four‑dimensional framework that links maqāṣid‑based values with environmental performance and methodological innovation, and highlight emerging opportunities for multidisciplinary, culturally responsive housing strategies in rapidly urbanising Muslim‑majority contexts.
Towards Safe Dissection: A Structured Multimodal State Representation for Laparoscopic Surgery
Introduction: The development of intelligent support systems for minimally invasive surgery requires careful awareness of how safety is maintained in automated decision processes. Deep learning has notably contributed to the analysis of laparoscopic recordings, improving anatomical segmentation, phase detection, and gesture recognition. However, most known approaches remain limited to predictions. Although they may not fully describe how the operational situation advances over time or how actions should respond to that evolution. Objectives: This work aims to give a structured state representation that helps safe learning in surgical conditions, where reward and imitation learning cannot depend on unrestrained exploration. Methods: Laparoscopic surgery is modeled as a sequential and partially observable process. A multimodal state is generated by merging anatomical context, a safety indicator derived from the Critical View of Safety, gesture dynamics, procedural phase information, and active instrument configuration into a cohesive picture. Results: Experimental evaluation shows that the proposed structured state architecture produces a coherent representation of the dynamic surgical environment, allowing trustworthy learning from recorded expert procedures. Conclusions: This study reveals that such structured state building provides a cohesive platform for dependable data-driven help in complicated Healthcare 5.0 scenarios.
Looking Through the Reading Glass: Enhancing ESL Students’ Reading Proficiency through English Language Exposure in the Classroom
Reading ability is a critical aspect in learning a second language and is a sure indicator of success for ESL students (Grabe & Stoller, 2019). ESL students’ reading abilities are greatly dependent on the extent to which they are exposed to the language, not only as a subject to be learned but also as a means of communication (Krashen, 1985; Ellis, 2008). In this piece, we will discuss how learning activities, exposure to language, and linguistic knowledge all come together to enhance reading abilities. It will discuss how vocabulary, grammar, and pronunciation are all key in improving reading abilities for ESL students (Nation, 2013; Koda, 2007). It will also discuss how the incorporation of technology and language-rich environments can greatly enhance reading abilities for ESL students (Lin, 2014). The piece will culminate in a call to actions to incorporate English in ESL students’ learning to enhance their reading abilities.
The Effect of Temperature Towards ESL Students’ Reading Interest in the ESL Classroom
Students' motivation level, interest, and cognitive performance are greatly affected by the classroom environment. Among these physical aspects that have been found to have a significant effect on students' interest level and cognitive processing, especially during reading processes, is temperature. This study aims to synthesize relevant studies in environmental psychology, second language acquisition, and ESL to examine the effect of classroom temperature on ESL students' interest in reading.According to the review, the willingness of ESL students to read, their attention span, and their emotional comfort are all positively affected by pleasant temperature conditions, which are between 22°C and 25°C. Extreme temperature conditions may decrease the reading motivation and increase the mental workload. In order to enhance the reading motivation for ESL students, various suggestions have been proposed for teachers and school officials regarding the ideal thermal conditions for the surroundings.
“Trusting the Transition: The Role of Policy Credibility and Institutional Support in EV Continuance Intention”
Introduction: Governments worldwide increasingly promote electric vehicles (EVs) as a strategy to reduce greenhouse gas emissions, decrease fossil fuel dependency, and improve urban air quality. While policy initiatives such as financial incentives, tax benefits, and charging infrastructure development have accelerated EV adoption, the long-term sustainability of electric mobility depends not only on initial adoption but also on users’ continuance intention. In emerging EV ecosystems, uncertainty regarding policy stability and institutional readiness may influence users’ post-adoption evaluations and their willingness to continue using EVs. . Objectives: This study aims to examine how policy credibility and institutional support influence EV users’ continuance intention through the mediating mechanisms of confirmation and satisfaction within the framework of Expectation-Confirmation Theory (ECT). Methods: The study adopts a quantitative cross-sectional research design and collects survey data from registered electric vehicle users in Kerala, India. The relationships among policy credibility, institutional support, confirmation, satisfaction, and continuance intention are analysed using Partial Least Squares Structural Equation Modelling (PLS-SEM) through SmartPLS software. Results: The results reveal that both policy credibility and institutional support significantly enhance confirmation of expectations among EV users. Confirmation shows a strong positive influence on satisfaction, and satisfaction emerges as the most significant determinant of continuance intention. The findings further indicate that confirmation and satisfaction act as key mediating mechanisms linking institutional and policy factors to sustained EV usage behaviour. Conclusions: Mi tempus imperdiet nulla malesuada. Magna fermentum iaculis eu non diam phasellus vestibulum. Consectetur adipiscing elit duis tristique sollicitudin nibh sit amet commodo. Elit scelerisque mauris pellentesque pulvinar. Et malesuada fames ac turpis egestas maecenas pharetra convallis posuere. Elementum integer enim neque volutpat ac tincidunt vitae semper.
LLM-Augmented Academic Analytics: A Retrieval-Augmented Generation Framework for Real-Time Student Performance Intelligence in Higher Education
Higher education institutions face a persistent challenge in translating large volumes of student performance data into timely, actionable intelligence for faculty and academic advisors. Conventional learning analytics dashboards rely on pre-aggregated historical datasets and static visualizations that are incapable of addressing context-specific, real-time queries. This paper proposes the Retrieval-Augmented Academic Analytics Framework (RAAF) — a novel architecture that integrates Retrieval-Augmented Generation (RAG) with existing institutional BI infrastructure to enable natural language querying of live student performance data. Drawing on foundational work in enterprise RAG-BI integration (Pathoori, 2025), this study extends the paradigm to the specific constraints and governance requirements of higher education environments, including FERPA compliance, multi-source academic record systems, and the unique challenge of hallucination risk in high-stakes advising contexts. The RAAF framework is validated through a conceptual case study at a private liberal arts university, demonstrating a projected 64% reduction in time-to-insight for academic advisors and a structured approach to LLM-generated output governance. Findings suggest that RAG-based academic analytics represents a meaningful evolution beyond static dashboards and carries significant implications for early intervention systems, retention analytics, and institutional decision support.
Classification of Medicinal Plants and Their Diseases: Application to the AI-MedLeafX Dataset
Medicinal plants are essential to traditional medicine and affect more than 80% of the world's population. Their identification remains complex due to the diversity of species and diseases. This study presents an automatic classification model based on convolutional neural networks (CNNs) to distinguish 4 species of medicinal plants (Camphor, HariTaki, Neem, Sojina) and their health states (Bacterial Spot, Healthy Leaf, Shot Hole), i.e. 10 classes in total. The model was trained and evaluated on the AI-MedLeafX public dataset. The model achieves an accuracy of 88.06% (11.94% error), with the main confusion between Camphor Shot Hole and HariTaki Bacterial Spot. These results confirm the feasibility of a automated identification of medicinal plants. In this paper we are trying to develop and evaluate a deep learning model based on convolutional neural networks (CNNs) for the automatic classification of medicinal plants and their diseases from leaf images.
The Impact of Road Infrastructure on the Economic and Industrial Activities of Cities: Case of the East-West Highway in the Constantine Region of Influence, Algeria
In most countries, transport infrastructure makes a major contribution to socio-economic development. Given these factors, a growing number of studies have focused on the impact of road infrastructure on urban activities. However, a systematic review of the literature revealed no bibliometric studies dedicated to an in-depth examination of the impact of road infrastructure on the economy and business of cities. The main objective of this study is to analyze the state of global research on the impact of transport infrastructure on the economy and business, and to identify future trends in this research area. We will focus this analysis on a specific case: the East-West Highway of Algeria, the subject of our research thesis. The main steps in a systematic literature review are 1. Design of the research study, 2. Preparation of bibliometric results, and 3. Analysis and interpretation of the results. The total sample of publications analyzed was 257 documents, retrieved from the Scopus database (up to November 10, 2024). The VOS viewer was then used to map and visualize the bibliometric networks. The results include the most significant publications in the field, as well as the most frequently cited international contributors in these studies, the leading authors in the field, and the most frequently used keywords.
A Big Data-Driven Information System for Disease Prediction in Public Health: A Comparative Study of Machine Learning Approaches
With the fast growth of health data coming from electronic records, medical devices, and monitoring systems, we now have great opportunities for data-driven decision making. However, dealing with such a large amount of information is still a challenge for standard analysis techniques. In this paper, we show a comparative study of machine learning models within a Big Data framework to predict diseases in public health. We tested six different classification techniques: Naive Bayes, SVM, Random Forest, Gradient Boosting, XGBoost, and MLP. To get reliable results, the experiments were done on two well-known medical datasets (UCI Heart Disease and Pima Indians Diabetes) using a 10-fold stratified cross-validation method. Interestingly, the results show that Naive Bayes performs best for heart disease (83.78% accuracy), while Gradient Boosting is the leader for the Diabetes dataset (77.72% accuracy). These findings offer practical advice on how to choose the right model, while also considering the choices and trade-offs made during the process.
A Novel Approach for Scheduling Problems on Machine Single
In this paper, we will present a comparative study between two neighborhoods for solving scheduling problems on machine single to minimize the weighted sum of the task's end dates; since this problem is NP-hard in the strong sense, exact methods require a computational effort that increases exponentially with the size of the problem. Approximate methods allow this problem to be solved reasonably. In this paper, we present the tabu search metaheuristic; it aims to find an approximate approach to the problem under consideration. We present the tabu search method and detail the parameters and main steps of the proposed approach, and the results obtained are applicable in economics and industry.
Design and Usability Evaluation of a Virtual Reality–Based Information System for Solar Photovoltaic Awareness
Virtual reality (VR) has increasingly been adopted as a platform for interactive information systems that support user engagement and system understanding through immersive interaction. This paper presents the design, implementation, and usability evaluation of PV Gallery, a VR–based information system developed to support user awareness and system-level understanding of solar photovoltaic (PV) systems. The system integrates structured onboarding, interactive informational modules, and a task-based component placement mechanism within a unified VR environment. A usability evaluation was conducted with 21 participants using a structured questionnaire assessing system usefulness, ease of use, ease of learning, user satisfaction, and perceived understanding. Descriptive statistics, reliability analysis using Cronbach’s alpha, and independent-samples t-tests were employed to examine system usability and user perception differences. The results indicate high user satisfaction and interaction intuitiveness, with strong internal consistency observed across most usability dimensions. Differences related to comfort and perceived usefulness highlight the importance of ergonomic and inclusive design considerations in VR-based information systems. The findings demonstrate the feasibility of VR as an interactive information system for presenting multi-component technological systems and provide practical insights for the design and evaluation of user-centered VR systems in engineering and management contexts.
Data Systems as Autonomous Agents: Applying Agentic AI to Big Data Platforms
Enterprise data processing environments face increasing operational complexity that exceeds traditional manual management capabilities. Current Big Data platforms rely on reactive operational models that respond to system issues after they impact performance and user experience. This article introduces autonomous agent architectures that transform data platforms into intelligent systems capable of independent perception, reasoning, and action execution. The proposed framework integrates perception layers for comprehensive system monitoring, decision models that balance multiple competing objectives, and action orchestration mechanisms that implement optimizations automatically. Autonomous capabilities enable continuous performance tuning, intelligent failure recovery, dynamic cost optimization, and automated policy enforcement without human intervention. The architecture maintains scalability and fault tolerance characteristics while adding sophisticated reasoning capabilities that adapt to changing operational conditions. Implementation strategies offer practical deployment methods that reduce disruption while gradually adding autonomous features. The operational transformation enables proactive optimization that predicts and prevents issues before they affect system performance. Human-agent collaboration frameworks define effective interaction models that balance oversight with system autonomy. Risk mitigation strategies ensure safe autonomous operation through bounded decision-making and comprehensive safeguards. Performance evaluation metrics demonstrate significant improvements in operational efficiency, cost reduction, and system reliability through autonomous operation.
Financial Operation Platforms: Automated Reconciliation, Data Lineage, and Control Frameworks for High-Scale Payment Enterprises
Large-scale payment enterprises process billions of transactions across heterogeneous payment instruments, external processors, and regulatory jurisdictions, making the financial operations platform a foundational infrastructure component for ensuring monetary correctness, regulatory compliance, and enterprise-wide operational trust. Despite significant advances in distributed systems architecture, prevailing reconciliation frameworks remain constrained by batch-processing assumptions, fragmented data ownership models, and the absence of end-to-end lineage governance that spans domain boundaries — limitations that are architecturally incompatible with the near-real-time correctness demands of modern payment enterprises. This article defines a financial operations platform that functions in near real time and encompasses closed-loop fund integrity, governance of financial metadata across distributed domains, and end-to-end reconciliation across acquiring, settlement, clearing, and cash movement stages. The framework is built on domain-driven microservices, data mesh architecture, canonical financial models, and deterministic control checks, with the overarching objective of ensuring that no money is created or destroyed, ledgers remain permanently consistent, and financial leakage is detected with minimal delay. Illustrative validation scenarios drawn from enterprise-scale reference implementations demonstrate that automated, multi-layer reconciliation reduces discrepancy detection latency from batch-period intervals to sub-minute windows, while canonical data model adoption reduces cross-domain integration defects by more than sixty percent compared to point-to-point integration baselines.
Human-Guided Intelligent Operations for Multi-Cloud Kubernetes at Enterprise Scale
Operating thousands of Kubernetes clusters across public cloud, private cloud, and edge environments strains traditional monitoring, which relies on static thresholds and manual triage. This article introduces a way to manage operations that sees reliability as a data issue: it collects data from different sources, combines various signals into a single view, represents service connections as a changing graph, and identifies problems using advanced detection and reasoning methods. The platform closes the loop with a serverless playbook engine that executes remediation when confidence is high and guardrails are satisfied, while keeping humans in the control plane through clear explanations and previewable actions. In practice, such systems can compress mean time to detect from tens of minutes to minutes, reduce mean time to restore through targeted automation, and materially lower the operating cost of large microservice fleets without compromising safety or governance.
Reliable Event-Driven Processing in Distributed Systems: Stage-Aware Retries, Idempotency, and Exactly-Once Semantics
Reliable event processing and the achievement of exactly-once semantics for business-critical events in distributed systems require more than merely the right transactional semantics. Although event-driven architectures provide scalability and loosely coupled components, they introduce non-obvious failure scenarios that are difficult to reason about. This work is primarily a conceptual and analytical study: it synthesizes mechanisms and architectural patterns drawn from distributed systems literature, vendor documentation, and production system designs, evaluating their complementary roles within a unified reliability framework rather than reporting empirical measurements from a specific system deployment. Transactional APIs offer built-in correctness guarantees but have well-defined throughput limits and optimization targets that differ from those of production systems. Techniques such as exponential backoff help protect systems from transient failures by routing retried events out of the main processing pipeline, avoiding retry storms, and allowing unaffected events to continue processing at normal throughput. Dead letter queues extend this protection by allowing events that could not be processed to be safely reinjected once the root cause of a failure is repaired, ensuring that no event is dropped silently even under prolonged or severe failures. Stage-aware retry mechanisms — where workflows are explicitly checkpointed — ensure that, on failure, only the incomplete stages are replayed rather than the entire workflow. Idempotent API and protocol design guarantees that repeated execution of the same operation does not alter shared state beyond its initial application. Reactive batching strategies further regulate the flow of events under load, preventing throughput degradation from cascading into reliability failures. Workflow orchestration engines provide durable, centralized coordination of multi-stage event processing across microservice boundaries, enabling fault-tolerant execution that choreography-based approaches cannot achieve alone. Stateful stream processing frameworks enforce consistency through distributed checkpointing, allowing pipelines to recover from failures at the granularity of individual operators rather than entire workflows. Achieving exactly-once semantics requires all of these layers to be implemented together: infrastructure-level retries, stage-aware execution, idempotent interfaces, stateful checkpointing, and periodic reconciliation, because each addresses distinct failure modes that the others cannot handle alone.
Dropbox Dash and the Transformation of Knowledge Work: An AI‑Native Architecture for Enterprise Search, Answers, and Productivity
Knowledge workers today operate in an environment where information is abundant but fragmented across dozens of disconnected systems—documents, emails, chat threads, tickets, wikis, calendars, and SaaS applications. While large language models (LLMs) like ChatGPT show promise for conversational AI, they struggle in business environments because they can't access internal knowledge, have limited context, and lack strong security and permission controls. This article presents Dropbox Dash as an AI‑native knowledge platform designed to address these limitations. Dash unifies enterprise information retrieval, semantic understanding, and AI‑generated answers into a single production system that is secure, scalable, and enterprise‑ready. We describe the architectural innovations behind Dash, its role in reshaping knowledge work for small and medium‑sized organizations, and contributions as a founding engineering leader responsible for its search and retrieval infrastructure. The article demonstrates how hybrid retrieval, large‑scale indexing, and multi‑stage ranking can transform enterprise search into a productivity‑multiplying AI assistant—effectively positioning Dash as "ChatGPT for work."
Enhanced Photovoltaic Performance of Cu(In,Ga)Se2 Solar Cells Via Quantum Well Engineering: A Computational Optimization Study Achieving 28.53% Efficiency
This study presents a comprehensive computational investigation of Cu(In,Ga)Se2 (CIGS) quantum well solar cells (QWSC) using SCAPS-1D simulation framework to enhance photovoltaic performance through advanced band engineering. We demonstrate that the strategic implementation of quantum well structures within CIGS absorber layers significantly improves charge carrier dynamics and light absorption efficiency. Our optimized CIGS-QWSC configuration achieves a remarkable power conversion efficiency of 28.53%, representing a 31.5% improvement over conventional CIGS cells (23.93%). The quantum well design incorporates alternating CIGS barrier (Eg = 1.41 eV) and well (Eg = 1.18 eV) layers with optimized thickness of 10 nm each, creating favorable energy band alignment for enhanced carrier collection. Key performance metrics include: open-circuit voltage (Voc) of 1.101 V, short-circuit current density (Jsc) of 33.117 mA/cm², and fill factor (FF) of 78.27%. Comprehensive parametric optimization reveals that quantum well depth of 0.23 eV and carrier concentration of 2×1016 cm-3 provide optimal photovoltaic performance. The quantum confinement effects enhance absorption coefficient by 2.3× in the near-infrared region while maintaining excellent charge transport properties. This computational methodology provides crucial insights for next-generation thin-film photovoltaic technology development.
Autonomous Enterprise Application Integration Using AI Agents and Event-Driven Cloud Architecture
As companies rapidly embrace digital transformation, legacy rule-based Enterprise Application Integration (EAI) techniques are proving increasingly unable to cope with the growing number of systems to integrate, continual change and the need for real-time processing․ SAP-centric IT landscapes with SAP S/4HANA, SAP Business Technology Platform (BTP), Software as a Service (SaaS) and heterogeneous non-SAP systems require a different kind of smart adaptive integration․ This article presents the AEAI pattern in SAP environments based on AI agents, event-driven cloud architecture and cloud-native integration services, by bringing together AI-driven decision making, self-healing integration flows, predictive scaling and event-based communication patterns․ Together, these help enterprises to automate integration, increase process resilience and governance, and respond to business events almost in real-time․ In this paper, we detail AI agents for the design, monitoring, optimization, security and remediation of integration scenarios․ This represents a shift from reactive integration management solutions to proactive integration ecosystems․
Achieving Near-Zero Downtime: A Technical Case Study in Oracle Database Resiliency
The order management and logistics platforms used by enterprises require continuous availability, but the conventional migration of the Oracle database schema creates extended service interruptions that do not coincide with the current business continuity demands. This article records the experience of scaling down the Oracle database deployment time lag from an alarming starting estimate to virtually zero by technical optimization and architectural evolution. The article started with a release management dry run that showed that there was a multi-day downtime need that was far beyond acceptability limits and posed a threat to order processing, forward logistics, and reverse logistics pipelines. The deployment window was shortened out of successive stages of Oracle database performance tuning, deployment process parallelization, sophisticated schema and transaction commit optimization, strategic scaling of infrastructure to higher-capacity processors, and, finally, the adoption of Oracle Edition-Based Redefinition, a potential breakthrough for near-zero downtime. Edition-Based Redefinition turned out to be the key enabling factor, enabling pre-upgrade and post-upgrade versions of applications to live in the same schema at the same time, and the cutover operation has been reduced to an instant metadata switch. The confidence to validate the solution before the production deployment was given by a predictive performance model that was calibrated using dry runs on a non-production environment and production telemetry. The article was a repeatable, near-zero downtime deployment model of all key Oracle-based services, which directly supported the larger "Always-On" availability program of the enterprise and provided a new standard of service resilience in large transactional settings.
Modern Workflow Automation Architecture Patterns: Contemporary Design Approaches for Enterprise Digital Transformation
The architecture of modern workflow automation has developed an improvement of monolithic architecture to advanced designs that are decoupled to workflow logic and service implementations. Five essential patterns of architecture: API-first design, event-driven orchestration, intelligent automation with AI capabilities, governance-built compliance, and modular component-based architectures are all essential to build flexible and resilient automation platforms by enterprises. The API-first approaches put into place clear contracts that guard against changes in service implementation to downstream consumers and allow flexibility of technology and multitasking development. Event-driven architectures embrace real-time responsiveness and asynchronous processing, which is the basic way to enhance system resilience by using loose coupling and failure isolation. Intelligence-enhanced automation combines machine learning and explainable AI to allow adaptive decision-making and still ensure governance and compliance control. Compliance checking and auditability start to be built into the workflow design through governance-conscious automation, and compliance checking turns into an inseparable part of operational components. Modular workflow architectures break down complex systems into general components, which can be scaled independently and speed up development with component libraries. A combination of these complementary patterns can help organizations to gain higher performance in terms of operational effectiveness, deployment speed, system reliability, and compliance, provide rapid digital transformation and handle architectural complexity based on cloud-native values and distributed systems best practices.