International Journal of Computational and Experimental Science and Engineering
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Earth-Air Heat Exchanger Systems for Energy Efficiency Enhancement in Algerian Residential Buildings: A Multi-Climate Zone Analysis
This study investigates Earth-Air Heat Exchanger (EAHE) systems for improving energy efficiency in Algerian residential buildings across diverse climatic zones. Algeria's climate conditions, ranging from Mediterranean coastal areas to hot arid desert regions, present unique challenges for passive cooling and heating technologies. This research analyzes EAHE performance in three representative cities: Tamanrasset (hot arid), Constantine (semi-arid Mediterranean), and Adrar (hot desert), using comprehensive meteorological data and building energy simulation models. The study employs mathematical modeling incorporating heat transfer analysis, soil thermal properties, and building energy dynamics. Monthly and hourly analyses used actual climate data with ambient temperatures ranging from 7.5°C to 35.5°C and solar irradiation values from 5.4 to 12.2 MJ/m²/day. The modeled EAHE system consists of 100-meter underground pipes buried at 3-meter depth, serving typical 150 m² residential buildings. Results demonstrate significant energy efficiency improvements across all climate zones. Annual energy savings ranged from 24.2% (Constantine, 1,892 kWh) to 31.3% (Adrar, 3,156 kWh), with Tamanrasset achieving 28.5% reduction (2,847 kWh). EAHE effectiveness values ranged from 65% to 82%, with peak performance during extreme ambient conditions. Hourly analysis revealed 4-8°C temperature reductions at EAHE outlets during peak summer, significantly reducing cooling loads. Economic analysis indicates annual cost savings of $427-$473 per household with 6-8 year payback periods, demonstrating economic viability. This research contributes practical guidelines for EAHE implementation in North African residential buildings, supporting earth-coupled heat exchange systems as effective strategies for reducing energy consumption and enhancing thermal comfort in Algeria's challenging climates.
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Biogenic Synthesis and Characterization of Zinc Oxide and Titanium Dioxide Nanoparticles Mediated by Plant Extracts
Abstract: Context: Nanotechnology, a multidisciplinary field involving physics, chemistry, and biology, offers major applications in biomedicine and dentistry due to the unique properties of nanomaterials. The green synthesis of nanoparticles (NPs), particularly from plant extracts, is a sustainable approach with advantages in terms of biocompatibility and reduction of harmful effects. Metal oxide nanoparticles, such as ZnO and TiO₂, are distinguished by their photocatalytic, magnetic, and antimicrobial properties due to their small sizes and large specific surface areas. In this study, ZnONPs and TiO₂NPs were synthesized using aqueous extracts of clove and olive leaves with a zinc salt (Zinc Nitrate) as pioneers and titanium dioxide due to its ability to catalyze chemical reactions and its efficiency in removing contaminants from the environment. Materials and methods: Different experimental techniques are used for the preparation, characterization and analysis of these nanoparticles, highlighting the ecological approach adopted in their synthesis. X-ray diffraction (XRD), infrared spectroscopy (FTIR), UV-visible spectroscopy, surface area determination analysis (BET) and X-ray fluorescence (XRF) were used to characterize the optical, chemical, crystalline, textural and elemental properties of the formed nanoparticles. Results: The green synthesized ZnONPs and TiO2NPs were initially confirmed using a UV-visible spectrophotometer. This technique was used to evaluate the optical properties of the nanoparticles and estimate the band gap size. The analysis allowed us to compare the absorption bands of the two types of nanoparticles, which eliminated interferences due to the solvent or the reference substrate, and to obtain an accurate absorption spectrum of the studied nanoparticles. Other analyses were carried out on these nanoparticles such as infrared (IR) spectroscopy analysis which allowed us to identify the functional groups involved in the stabilization of the nanoparticles. X-ray diffraction (XRD) was used to determine the crystal structure and crystallite size of the nanoparticles. The Brunauer–Emmett–Teller (BET) technique was used to measure the specific surface area of nanoparticles, a crucial data for catalytic and absorptive applications. The antimicrobial activity was tested for TiO₂ and ZnO nanoparticles synthesized from plant extracts against two bacterial strains: Staphylococcus aureus, Escherichia coli. Titanium dioxide nanoparticles showed the best overall efficacy against Gram-positive and Gram-negative bacteria while ZnO nanoparticles showed fair efficacy, but lower than TiO2 in the majority of cases. Conclusion: The study of antimicrobial activity highlighted a notable efficacy, in particular for TiO₂ nanoparticles associated with olive leaf and clove extracts, demonstrating a strong inhibitory power on Staphylococcus aureus and Escherichia coli.
Enhancing The Performance and Sustainability of Lightweight Hebel Bricks Using Local Coco Fiber Additives: A Green Material Innovation for Circular Construction
The construction industry faces increasing pressure to adopt sustainable and low-carbon materials, particularly in the production of lightweight concrete such as Hebel bricks. This study investigates the incorporation of locally sourced coco fibre, an agricultural waste derived from coconut husks, as a natural additive in Hebel brick mixtures. The novelty of this research lies in the utilization of coco fibre—abundant, biodegradable, and mechanically resilient—as both a performance-enhancing component and an ecological solution for Indonesia’s construction sector. By improving the mechanical properties of lightweight bricks while promoting resource circularity, this approach contributes to green material innovation and addresses waste valorisation challenges in coconut-producing regions such as North Sulawesi. An experimental method was employed using five mix proportions with coco fibre contents of 0% (control), 0.5%, 1.0%, 1.5%, and 2.0%. Compressive strength and flexural strength tests were conducted in accordance with ASTM C109 and ASTM C348, respectively, at curing ages of 3, 7, and 28 days. The results demonstrate that compressive strength increased significantly with coco fibre addition up to an optimal content of 1.5%. At 28 days, the optimal mixture achieved a compressive strength of 3.42 MPa, exceeding the minimum requirement of 3 MPa specified by SNI 03-0349-1989 for lightweight concrete blocks. Furthermore, the bulk density of all mixtures remained below 1,000 kg/m³, confirming their suitability for non-structural lightweight construction applications. Overall, this study confirms that coco fibre incorporation enhances mechanical performance while supporting sustainable construction practices, circular economy principles, and the development of environmentally responsible building materials.
Evaluating Social Housing Suitability in Oasis Cities Using GIS-Based Multi-Criteria Decision Analysis: A Case Study of Bou Saada, Algeria
This research presents a comprehensive assessment of habitat suitability in oasis environments by integrating advanced spatial analysis with multi-criteria decision-making methods. Oasis settings face specific climatic, cultural and spatial constraints that require analytical frameworks capable of capturing local complexity. To address this, the study uses a combined geographic information system (GIS) and analytic hierarchy process (AHP) approach to evaluate social housing suitability in three neighborhoods in Bou Saada, Algeria: the traditional old town (Kasser), the standardized 270 social housing development, and the newly planned AADL neighborhood.The analysis is structured around a multi-criteria framework that includes four key dimensions: environmental-climatic factors (34.7%), socio-cultural criteria (29.8%), functional and design aspects (20.3%) and economic-sustainability considerations (15.2%). Criterion weights were obtained using the AHP method, with consistency ratio verification (CR < 0.10) to ensure reliable pairwise comparisons. The results show significant variations in suitability between districts. The Old Town received the highest score (8.5/10), which reflects strong climate adaptability, cultural continuity and community cohesion. The AADL area was second (6.4/10), demonstrating moderate but better performance. In contrast, 270 social housing sector showed the lowest suitability (4.4/10), which indicates significant design limitations and weak contextual integration.These findings confirm that traditional urban forms provide inherent adaptive advantages in oasis environments. In addition to demonstrating the effectiveness of GIS-AHP integration at the neighborhood scale, the study provides practical insights for planners and policy makers engaged in developing climate-responsive, culture-based housing strategies for arid regions.
Profitability Impact of AI Adoption in Annuity Pricing
The insurance enterprise faces growing challenges in optimizing annuity pricing strategies under changing market situations and growing opposition to its product portfolios. Conventional actuarial models have massive deficiencies, as they depend upon historical data patterns and linear assumptions that cannot capture complex, nonlinear relationships among risk factors and profitability results. The usage of machine learning algorithms in annuity pricing frameworks truly addresses core shortcomings in conventional methodologies, with particular regard to mortality risk assessment, sensitivity analysis of interest rates, and policyholder behavior prediction. Gradient boosting decision trees, neural network architectures, and ensemble methods applied to fixed and indexed annuity products achieve superior predictive accuracy compared to generalized linear models commonly found in traditional actuarial practice. This implementation framework integrates machine learning predictive capabilities with established actuarial practices to ensure regulatory compliance while maintaining mathematical soundness. Deep learning approaches to mortality forecasting, especially Long Short-Term Memory networks, transcend restrictions of classical Lee-Carter models by allowing temporal dependencies and nonlinear patterns to be captured, characteristic of modern mortality experiences. Reinforcement learning applications to derivative hedging strategies optimize dynamic rebalancing decisions for indexed annuity products with embedded options. Quantitative comparison among stochastic mortality models across heterogeneous populations reveals performance differences conditional upon demographic characteristics and projection horizons. Spread margin enhancement through predictive analytics allows a more sophisticated crediting rate determination and policyholder retention strategy. ROI considerations include not only direct improvements in profitability but also indirect operational efficiency gains, balanced against infrastructure and personnel investment requirements.
AI-Driven Autonomous Treasury Orchestration: A Next-Generation Framework for Global Liquidity Optimization
This article presents an overall framework for AI-enabled autonomous treasury orchestration, transforming traditional rules-based cash management into an intelligent, self-optimizing system capable of making real-time decisions across global liquidity operations. It combines four core technological pillars: reinforcement learning algorithms for dynamic investment allocation, predictive cash flow modeling using advanced time-series architectures, adaptive risk management systems that react to market conditions and evolving counterparty profiles, and explainable AI mechanisms that ensure regulatory compliance and auditability. Traditional Treasury Management Systems execute on hardwired decision trees, which cannot adapt to the emergence of turbulent market conditions, unexpected cash flow disruptions, or changing risk profiles. Large pieces of potential optimization value cannot, therefore, be realized. To address this critical gap, the contribution of this study is to develop an autonomous orchestration architecture that enables AI agents to continuously learn from historical patterns and predict future liquidity needs with increased accuracy, while executing allocation strategies that balance the competing objectives of yield maximization, risk minimization, and liquidity preservation. The multi-agent system design within the framework enables specialized agents for prediction, optimization, execution, and monitoring to cooperate towards unified organizational goals, with robustly designed governance controls and human oversight mechanisms. Validation through simulation environments and backtesting frameworks reflects that AI-augmented approaches achieve superior risk-adjusted performance compared to static rule-based systems. Contributing valuable implementation guidance for financial institutions pursuing digital transformation of treasury operations, the article addresses the challenges of integrating with legacy systems, regulatory compliance requirements, and issues related to organizational change management that are crucial for the successful deployment of autonomous treasury technologies.
Smart Agriculture in the Fight against Weeds: Analyzing the Impact of Image Quality on Deep Learning Performance
Weed management is a critical challenge for sustainable agriculture, driving the adoption of Smart Farming solutions that integrate the Internet of Things (IoT) and deep learning. While significant research focuses on improving object detection algorithms, the influence of data quality from IoT perception devices on system performance remains underexplored. This paper presents a holistic study of an IoT-aligned weed detection framework, using the YOLOv8 model to investigate the impact of image data quality versus quantity. We train and evaluate two identical YOLOv8n models on contrasting datasets: a high-quality, smaller dataset (D1, n=512) and a larger, lower-quality dataset (D2, n=5,061). Our results show a decisive advantage for data quality: the model trained on D1 achieved a mean Average Precision (mAP@50) of 0.90, significantly outperforming the model trained on D2 (mAP@50 of 0.82), alongside higher precision (0.88 vs. 0.74). This empirical evidence underscores that for robust IoT-based detection systems, investing in high-fidelity data acquisition at the Perception Layer is more effective than merely amassing larger volumes of data. The findings offer a practical design principle for developing reliable and efficient smart agriculture solutions, emphasizing the need for system-level optimization beyond algorithmic choice.
Designing Scalable CI/CD Pipelines for Regulated Enterprises Using Kubernetes and GitOps
Legacy software delivery practices pose difficulties in regulated industries such as financial services, healthcare, and government, where organizations must comply with governance requirements throughout the software delivery lifecycle while protecting sensitive information. Most common continuous integration and continuous delivery CI/CD pipelines lack auditability and traceability and include manual processes, which are a bottleneck in the release process to production systems. Environment inconsistencies lead to deployment failures and configuration drift across infrastructure tiers. The article presents an architectural framework combining Kubernetes orchestration with GitOps methodology for regulated enterprise environments. Declarative configuration management establishes Git repositories as authoritative sources for infrastructure state. Pull-based deployment models eliminate direct pipeline access to production clusters. Zero-trust security principles ensure continuous verification of access requests regardless of network origin. Policy-driven automation embeds compliance validation throughout the build and deployment stages. Admission controllers enforce governance rules at deployment time without manual intervention. Comprehensive observability mechanisms provide audit capabilities satisfying regulatory examination requirements. The framework enables organizations to accelerate deployment frequency while preserving rigorous change management controls. Separation of duties occurs naturally through pull request approval workflows. The architectural patterns presented address fundamental gaps in traditional CI/CD implementations for highly regulated operational contexts.
Cloud-Native Architectures and the Evolution of Financial Risk Management Systems
Today's financial institutions are under unprecedented pressure to manage compliance operations with increasing regulatory requirements, sophisticated financial crimes, and exponential data growth. Cloud-native architectures represent a paradigm shift in financial risk management by adopting microservices frameworks, streaming analytics, and artificial intelligence for real-time fraud detection and automated compliance verification. From monolithic legacy systems to distributed, cloud-native platforms, Anti-Money Laundering and Know-Your-Customer workflows will be fundamentally redesigned to help institutions move from hours to milliseconds of detection latency, while reducing false positives and operational costs. Independent development, deployment, and scaling of compliance modules for improved system resilience and reduced innovation cycles are made possible by microservice designs. Using streaming data pipelines with tools like Apache Kafka and Apache Flink, transaction monitoring and behavioral analysis may now be done continuously over several channels. This replaces proactive, real-time intervention with historical batch processing. With automated compliance monitoring systems, great gains in operating efficiency, cost savings, and regulatory compliance are achieved. Ongoing understanding of system performance is made possible by sophisticated observability paradigms. Particularly, Kubernetes, container orchestration solutions provide flexible and scalable methods for deploying challenging compliance features throughout a broad spectrum of institution sizes; while hybrid cloud against computational scalability, architectures balance data sovereignty needs. Technological changes, then, move cloud-native compliance infrastructure to center stage for enabling financial integrity, institutional trust, and competitive differentiation in an ever-more-complicated regulatory landscape.
Computational and Experimental Evaluation of Secure Firmware Development Using Rust and AI‑Driven DevOps Automation
This study presents a computational and experimental evaluation of secure firmware development using the Rust programming language integrated with AI‑driven DevOps automation. Modern firmware engineering continues to face challenges related to memory‑safety defects, multi‑architecture build complexity, and manual continuous‑integration configuration. To address these issues, the proposed framework combines Rust’s ownership‑based compile‑time safety guarantees with multi‑target cross‑compilation pipelines for x86‑64 and ARM, QEMU‑based hardware‑in‑the‑loop simulation, and machine‑learning‑assisted automation incorporating gradient‑boosted decision trees, natural language processing techniques, and multi‑agent orchestration for pipeline synthesis, compliance prediction, and diagnostic analysis. Experimental validation was performed using Azure DevOps infrastructure and included systematic benchmarking with paired t‑tests (n = 30 per configuration), bootstrap confidence intervals (10,000 iterations), and coefficient of variation analysis to ensure statistical robustness. The evaluation integrates cargo‑based testing, QEMU emulation, and automated performance‑regression detection.Results demonstrate complete elimination of memory‑safety vulnerabilities in Rust components, a 90–95% reduction in developer onboarding time, a 75–85% decrease in build failure‑resolution effort, and performance parity with optimized C++ implementations (p < 0.05). Reliability also improved, with defect‑escape rates approaching zero during production deployment. Overall, the findings validate Rust’s suitability for security‑critical firmware and highlight the engineering benefits of incorporating AI‑assisted DevOps workflows. The study provides reproducible computational methods, experimental protocols, and implementation patterns for organizations seeking scalable, memory‑safe, and automated firmware development practices.
Lalith Lakshmi Chaitanya Kumar Mangalagiri
Self-Healing RPA Systems: A Reliability-Centric Architecture for Financial Enterprises
Financial​‍​‌‍​‍‌​‍​‌‍​‍‌ institutions are struggling to keep their robotic process automation reliable, even though their operational environments are constantly changing. Traditional RPA deployments are very fragile and, as a result, are affected by changes in the interface, variations in the infrastructure, and even changes in the data formats. Manual intervention requirements undermine automation value propositions by extending recovery timelines and consuming support resources. The self-healing architecture presented addresses fundamental limitations through integrated telemetry capture, intelligent diagnostics, adaptive recovery mechanisms, and continuous learning capabilities. Multi-layer designs embed autonomous corrective logic directly within RPA execution frameworks rather than relying on external monitoring systems. Real-time telemetry streams enable pattern recognition algorithms to classify failure types and route incidents to appropriate remediation procedures. Adaptive selector management maintains hierarchical fallback chains spanning multiple element identification strategies. Exception routing logic distinguishes transient faults amenable to automated recovery from structural defects requiring human expertise. Financial operations implementations demonstrate practical applications across payment processing, account reconciliation, and regulatory validation workflows. Reliability engineering principles establish observability frameworks, measuring recovery efficacy and diagnostic accuracy. Continual learning architectures refine classification models through feedback loops, capturing production outcomes. The architectural framework transforms automation reliability from static design properties into dynamic operational capabilities evolving alongside environmental changes.
Cloud-Native Workflow Automation in Pharmacy Benefits Management: A Case for Hybrid Orchestration
This composition delves into cloud-native workflow mechanisms within Pharmacy Benefits Management sectors through innovative hybrid coordination strategies blending traditional Process Management frameworks with contemporary cloud infrastructures. The fusion of sophisticated case oversight functions, container virtualization, stimulus-responsive frameworks, and decentralized service components establishes a formidable structure addressing distinctive PBM operational hurdles. Examining architectural foundations alongside practical implementations—encompassing medication adherence pathway refinements, authorization verification systems, and pharmaceutical transportation logistics—reveals how this merged approach yields unparalleled flexibility, expansion capacity, and operational continuity compared with conventional techniques. Domain-specific structural philosophies, message-driven interaction patterns, and multidisciplinary execution tactics emerge as decisive achievement elements throughout these ventures. The architectural configuration portrayed offers a groundbreaking answer for PBM entities pursuing enhanced functional productivity while preserving administrative oversight essential within strictly regulated healthcare landscapes.
Adaptive AI Model Selection for Cloud Infrastructure Optimization: A Framework for Intelligent, Self-Regulating Computing Environments
Contemporary cloud computing ecosystems demand intelligent infrastructure management strategies that transcend traditional static provisioning models. The adaptive AI model selection framework presented herein addresses fundamental challenges in managing heterogeneous workloads across microservices architectures, data processing pipelines, IoT data streams, and AI inference engines through systematic integration of machine learning operations with event-driven automation mechanisms. The framework synthesizes cloud engineering principles, Infrastructure as Code methodologies, and intelligent model selection algorithms to enable real-time optimization based on telemetry analysis, historical workload patterns, and operational objectives. Through automated training pipelines, continuous evaluation protocols, and progressive deployment strategies with rollback capabilities, the framework facilitates a self-optimizing infrastructure that minimizes human intervention while maintaining service-level agreements and cost efficiency. The architecture comprises telemetry collection subsystems, versioned model repositories, intelligent selection engines implementing multi-criteria decision frameworks, and automated deployment orchestration utilizing canary patterns and circuit breakers. Event-driven automation enables real-time responsiveness through stream processing frameworks that evaluate optimization opportunities via windowed computations, complex event processing patterns, and stateful processing mechanisms. Enhancements to multi-cloud and hybrid environments support heterogeneous resource abstractions, cross-platform data movement limits, and vendor-specific operational behaviors by using cloud-agnostic abstraction layers and provider-specific adapters. The framework illustrates how smart and dynamic infrastructure operation can make organizations realize better resource utilization, better service reliability, and reduction of operational expenses in da distributed computing environment, as well as provision of regulatory compliance and data sovereignty requirements in a complex multi-cloud deployment.
Becoming a Cloud-Native Architect in the LLM Era: Skills, Tools, and Mindset for the Next Generation
Cloud-native approaches have reshaped the software architect’s role from producing static designs to guiding decisions in complex distributed systems. In the LLM and AI era, architects increasingly treat foundation models as production dependencies, which introduces probabilistic behavior, new security threats [14], and operational quality metrics beyond availability and latency. This evolution demands both deep technical expertise, including containerized workloads, microservices, transient infrastructure, Kubernetes orchestration, service mesh, infrastructure-as-code, observability, and practical AI and LLM systems knowledge, including retrieval architectures and AI agent architectures, and strong organizational capability for collaboration and strategic decision-making. Effective practice relies on distributed-systems patterns for consistency, fault tolerance, and resilience, and on cross-functional leadership with product, security, and business stakeholders. Skill growth is reinforced through structured design reviews, platform engineering work, incident response, open-source contributions, and internal platform development. Core technologies typically include Kubernetes, service mesh implementations, automation tooling, observability stacks, CI/CD frameworks, and AI and LLM infrastructure for model serving, vector search or retrieval components, and agent orchestration. Operating at the intersection of technology and business strategy, cloud-native architects must also define and run AI quality and safety objectives, for example factuality and hallucination reduction, robustness, privacy, and security, supported by evaluation pipelines and governance controls [11, 12, 14, 19].
Intelligent Automation in Healthcare IT: Advancing Operational Resilience through Event-Driven Validation Frameworks
Event-based automation systems are a revolutionary paradigm in the healthcare information technology activities that respond to the critical issue of operational resiliency in the growing and increasingly complex digital health ecosystems. The SupportPlus model proves how intelligent automation may radically rethink the validation process, moving away from a manual process of reactivity towards validation to a system-driven mechanism of assurance. Using microservices architecture, serverless computing, and container orchestration on the Azure Kubernetes service, the framework integrates validation, monitoring, and compliance functions in a metadata-based ecosystem. Application to mission-critical healthcare services also demonstrates significant operational benefits such as radical decreases in manual validation cost, faster incident response settings, and system availability that is approaching five-nines reliability. The architecture uses event-driven orchestration to instigate automated validation gates built into continuous delivery pipelines, modifying compliance with periodic manual audits into continuous compliance processes. The self-healing functionality is defined as the capability of the system to automatically recover most of the validation failures, and this aspect is fundamentally changing the dynamic between the operations of IT and the system reliability. In addition to the short-term efficiency, the framework harmonizes the validation practices throughout organizational teams, eradicates tribal knowledge dependence whatsoever, and develops ongoing compliance evidence that can be examined during a regulatory audit. The measurable effect proves that intelligent healthcare IT automation is not only cost optimization but also meets the main demands of patient safety, care continuity, and trust of people in digital health infrastructure.
Bioaggressors of the olive tree in a semi-arid region: identification, characterization, and biocontrol alternatives using essential oils of Rosmarinus officinalis and Cupressus sempervirens
The olive tree represents a major arboricultural resource in Algeria; however, its cultivation is threatened by various bioaggressors. This study aims to inventory fungal infestations and olive pests in the Laghouat region and to evaluate the effectiveness of natural alternatives to chemical pesticides. The methodology is based on three main approaches:(i) identification of pathogenic fungi sampled from different parts of the olive tree and the surrounding soil;(ii) extraction by hydrodistillation of essential oils (EOs) from Rosmarinus officinalis and Cupressus sempervirens;(iii) evaluation of their in vitro antifungal activity through direct confrontation against phytopathogenic fungi, combined with an inventory of entomofauna using various trapping techniques.The results led to the identification of five fungal strains (Fusarium solani, Fusarium sp., Penicillium sp., Colletotrichum sp., and Alternaria sp.), with extraction yields of 0.51% for R. officinalis and 0.39% for C. sempervirens. Antifungal tests revealed that pure rosemary essential oil exerted total inhibition (100%) against F. solani and significant inhibition (80%) against Fusarium sp., whereas cypress essential oil showed inhibition rates of 66.67% and 71.43%, respectively. Even at reduced concentrations (25% and 50%), both essential oils maintained inhibitory activity exceeding 50%.The entomological survey identified 11 species, including 5 natural predators, with a predominance of Hymenoptera (29%). These results confirm the potential of essential oils as promising biopesticides for the sustainable management of olive tree bioaggressors.
DevOps for Diagnostics: Automated Continuous Integration/Continuous Deployment (CI/CD) of Multi-Modal AI Models into Hospital PACS and LIS
Diagnostic healthcare has been confronted with a consistent problem of deploying multi-modal models of artificial intelligence into the current systems of Picture Archiving and Communication Systems (PACS) and Laboratory Information Systems (LIS) because of the long-term implementation cycle, version management difficulties, and workflow interruptions. Continuous Integration/Continuous Deployment pipelines based on DevOps are radically innovative in that they automate the entire lifecycle of diagnostic AI models, starting with their creation up to their on-production maintenance. Kubernetes-based cloud-native architectures have the scalability, resilience, and compute efficiency required to handle computationally intensive diagnostic loads, as well as to regulate and govern data. The proposed framework will enforce automatic model versioning, drift detection policies, multi-phase validation policies, and a smooth integration with hospital enterprise systems via DICOM and HL7 interfaces. Empirical assessment in various healthcare facilities proves significant success rates in deployments, system stability, and system processing time without affecting clinical operations and diagnostic results during automated retraining loops. Incremental deployment models, such as canary releases and full monitoring infrastructure, allow safe updating of models without interfering with running clinical processes. Federated learning has the capability of integrating to enable the multi-institutional models to become improved, coupled with privacy preservation and regulation limitations on data sharing. Clinical acceptance evaluations indicate that there is little workflow interference and a gradual increase in user confidence due to the coherent model activity and dependability. The framework is effective in considering the main issue of long-term effectiveness because over time, the population of patients, the prevalence of the disease, and clinical practices may change. Computerized governance protocols and model registry centralized offer institutional supervision and decrease the IT support load, and facilitate the scalable implementation of AI-powered diagnostics in heterogeneous healthcare settings.
From Legacy to Cloud: A Case Study in Consolidating Insurance Producer Compensation Platforms
The insurance sector has a massive challenge of getting control over producer compensation based on the broken legacy systems that have been developed over decades of organic expansion and mergers. In this case study, the authors describe the process of transformation of a large insurance company that managed to dismantle several old-fashioned compensation systems to create one common cloud-based system. The project solved some of the most demanding business pains, such as data duplication, high maintenance fees, compliance risk, and lack of operational flexibility. The organization realized significant operational savings through careful fostering of architectural choices using microservices design patterns, careful data migration planning with periodic verification procedures, and an intelligent internal integration process between licensing and compensation modules. Regulatory compliance was assured through the use of real-time validation frameworks to check the credentials of any agent before the actual processing of payments, and the cloud infrastructure ensured the scalability required during peak periods. The transformation provided a quantifiable output, such as substantial cost savings, drastic processing efficiency, improved data quality measures, improved compliance scores, and improved producer satisfaction scores. The flexible architecture of the platform allowed deploying new compensation programs quickly, which gave competitive advantages in the environment of the dynamically evolving market. The experience gained during this enterprise-wide modernization project provides a serious lesson to any insurance carrier considering similar digital transformation initiatives that strategic investments in technology can have a fundamental transformation in operational capabilities without affecting business continuity and regulatory compliance.
Decentralized Infrastructure and Yield-Bearing Stablecoins for Financial Inclusion
Old economy banking infrastructure systematically bars billions of people across the globe from fundamental financial services by way of insurmountable documentation barriers, exorbitant fee systems, and geographic reach that disproportionately affect developing economy populations. Local currency instability and hyperinflation further enhance these problems by decimating savings and buying capacity, locking communities in vicious cycles of economic instability. Blockchain and decentralized financial protocols appear as revolutionary solutions that democratize access to finance using only internet connectivity, removing intermediaries and institutional gatekeeping systems. Dollar-pegged stablecoins bring much-needed stability to volatility in currencies without sacrificing the accessibility advantages of distributed ledger infrastructure. Decentralized lending protocols produce legitimate returns by linking borrowers and lenders via algorithmic interest rate models, which are transparently operated without central decision-making power. Self-custody wallets function as complete pseudo-bank debts supplying global attain and continuous accessibility, allowing customers to keep, transmit, and hold digital property without requiring institutional approval or extensive documentation. Clever contracts execute mechanically primarily based on predetermined conditions, disposing of human intermediaries at the same time as ensuring transparency via immutable public blockchain information. Revolutionary regulatory frameworks establish sandbox environments that facilitate controlled experimentation with blockchain-based economic services, enabling innovation even as preserving customer protection requirements. Mobile-first user experience design with support for local languages answers the specific needs of developing market populations relying solely on internet access via mobile devices. Intersecting these technological advancements makes financially independent ecosystems possible for serving previously excluded communities through yield-producing instruments and barrier-free cross-border payment capabilities.
From Legacy to Leading Edge: AI’s Role in Modernizing Platforms Sahil Agarwal
Legacy modernization constitutes a formidable technical and strategic challenge for enterprises maintaining large-scale software infrastructures. Systems accumulate complexity through decades of incremental development, resulting in tangled dependencies, obsolete frameworks, and inconsistent application programming interfaces. Manual migration approaches prove costly and hazardous due to dependence on institutional knowledge that frequently disappears over time. Conventional methods involving manual code rewriting introduce defects, prolong system unavailability, and impede innovation cycles. Recent advances in artificial intelligence have fundamentally altered modernization methodologies. Contemporary intelligent migration frameworks synthesize code comprehension models, dependency graph analytics, and predictive validation mechanisms to automate substantial portions of migration workflows. Machine learning architectures now parse, categorize, and translate complex codebases while maintaining high degrees of semantic integrity, though challenges remain as language models occasionally fail to preserve complete semantic equivalence. These enhanced systems diminish human error during transformation operations and strengthen system dependability, facilitating modernization efforts at scales previously deemed impractical. This article explores architectural underpinnings, operational mechanisms, security protocols, and implementation challenges within these frameworks, illustrating their capacity to convert legacy modernization from episodic reconstruction initiatives into perpetual evolutionary maintenance processes.
Architectural Transformations and Planning Regulation Violations in Residential Housing Allotments: A GIS Approach in Zouaghi Slimane-Ain El Bey, Phase 2, Constantine (Algeria)
This paper investigates residential housing allotments in the city of Constantine, Algeria, with a particular focus on their compliance with urban planning regulations. The study adopts the urban residential area of Zouaghi Slimane, Ain El Bey, Phase 2, as a representative case study, as this subdivision is characterized by a high prevalence of urban planning violations committed by residents. The research aims to identify and analyze the various forms of urban violations, explore their underlying causes, and ultimately propose a set of recommendations to mitigate and limit the spread of such practices. Geographic Information Systems (GIS) are employed as the primary analytical tool, enabling the spatial identification, quantification, and visualization of violations. The findings highlight the effectiveness of GIS as a decision-support tool for urban control, monitoring, and sustainable urban management
Agentic AI Initiatives: Autonomous Database Operations in Databricks
This article presents the Databricks Agent Bricks framework for autonomous database management and demonstrates its effectiveness across PostgreSQL, MySQL, MongoDB, and SQL Server environments. The framework establishes a distributed multi-agent architecture with specialized database agents coordinating through intelligent abstraction layers and machine learning-driven decision algorithms. Reinforcement learning-based self-healing workflows enable predictive performance optimization, automated remediation, and intelligent indexing strategies based on historical patterns and real-time telemetry analysis. Integration with Apache Airflow supports dynamic backup DAG generation, cross-database consistency coordination, and intelligent scheduling that minimizes production impact during maintenance operations. Cloud-native patterns enable hybrid operation with Azure Flexible Servers while preserving comprehensive security frameworks, compliance automation, and cost optimization capabilities. Validation in representative enterprise workloads demonstrates that Agent Bricks reduces mean time to remediation by approximately forty-five percent, improves system availability by thirty-two percent, and lowers operational resource consumption by twenty-eight percent compared to traditional manual database administration approaches. Performance benchmarking across heterogeneous database environments confirms significant improvements in query response times, automated incident resolution, and proactive capacity management, providing empirical evidence for the transformative value of agentic AI implementations in enterprise database operations.
Cloud Migration Blueprinting and Integration Governance Frameworks: A Structured Approach to Enterprise Architectural Transformation
From a fairly basic technical task to a highly complex architectural problem requiring appropriate governance, precise semantics, and operational stability, cloud migration has undergone significant changes. Despite the cutting-edge automation systems and cloud-native solutions available, companies still struggle to create effective migration architectures, maintain reliable data integration, and ensure uniform governance across multiple hybrid and multi-cloud environments. The following paper proposes a comprehensive Cloud Migration Blueprinting and Integration Governance Framework that organizes decision-making, aligns architectural designs with corporate policies, and embeds governance principles directly into migration processes. The framework combines reference architecture modeling, metadata-driven integration controls, and policy-aware orchestration to reduce confusion, expedite migration preparation, and enhance tracking of data movement and transformation across cloud ecosystems. The model redefines how architects operate—not just as solution designers, but as governance enforcers who ensure that integration principles, compliance constraints, and lineage accountability are built in from the first blueprint sketch to final execution.
Edge-Deployed AI for Intelligent Financial Document Processing and Fraud Detection: A Technical Review
The proliferation of mobile banking and financial service applications has revolutionized document processing workflows with remote capture and validation capabilities. Traditional server-based systems for processing financial documents including checks, mortgage instruments, trust agreements, and tax compliance forms capture images and forward them to centralized computing infrastructure for optical character recognition and validation workflows, which expose weaknesses in terms of latency, privacy risks, and operational expenses. Contemporary edge computing paradigms allow artificial intelligence models to run directly on mobile devices, freeing up backend servers and processing sensitive financial documents in local settings. Resource-efficient anchor-free object detection networks designed for use in constrained environments enable real-time extraction of critical document components essential for validation and authentication workflows across diverse financial instrument types. Architectures deployed at the edge exhibit substantial benefits in the form of lower transaction latency, better privacy safeguarding with localized processing, lower infrastructure expenses, and increased reliability under connectivity-limited circumstances. Federated learning mechanisms facilitate ongoing model improvement without centralized sensitive data, maintaining user privacy while enhancing detection capabilities. Persistent challenges include model drift due to changing document designs, adversarial attack susceptibility, device security needs, and governance complexity for distributed deployment. Hardware-software co-design efforts hold out the promise of specialized neural processing units with custom operations supporting document intelligence tasks, allowing for more advanced capabilities within mobile form factors and power budgets.
Cloud-Enabled Trust and Transparency in Global Finance and Healthcare
The use of cloud computing technologies has transformed how the finance and healthcare industries build and sustain trust, transparency, and accountability in digital ecosystems. Architectures based on moral values and sound governance systems show significant potential to enhance social trust. Improved data interoperability, real-time audit capabilities, and equitable access to computational resources drive this transformation. The combination of artificial intelligence and distributed ledger technologies, along with standardized healthcare information exchange protocols, offers the potential to enhance reporting accuracy by approximately 40%, reduce fraud detection time by 60%, and expand healthcare services to underserved populations. Challenges include algorithm bias, data sovereignty questions, market concentration, and regulatory models balancing privacy protection with innovation. Secure cloud adoption relies on standards like ISO/IEC 27018 and governance frameworks, including the Federal Risk and Authorization Management Program. Edge computing, serverless systems, and quantum integration indicate continued change. This work presents an original analysis of how cloud computing's promise as a digital infrastructure for societal equity requires dedication to transparency by design, governance approaches spanning organizational functions, and policy frameworks guaranteeing universal access regardless of income level or geographic location.
The Digital Backbone of Modern Manufacturing: Understanding PLM and ERP Integration
The contemporary manufacturing landscape necessitates sophisticated digital architectures that seamlessly orchestrate product development and operational execution through integrated enterprise systems. This article explores the fundamental roles and synergies between Product Lifecycle Management (PLM) and Enterprise Resource Planning (ERP) systems, examining how these platforms function as the strategic intelligence layer and operational engine of modern manufacturing enterprises, respectively. The article demonstrates that PLM systems serve as intellectual repositories managing product information from conception through retirement, while ERP platforms provide the transactional backbone for day-to-day operations, including procurement, production scheduling, and financial management. Through a comprehensive analysis of system integration points, the article reveals how the digital thread connecting PLM and ERP creates bidirectional data flows that transform fragmented business processes into cohesive operational workflows. The implementation of integrated PLM-ERP architectures enables organizations to achieve unprecedented transparency, eliminate information silos, and establish closed-loop feedback mechanisms where operational data systematically informs product development decisions. The articles indicate that successful integration delivers substantial improvements across multiple performance dimensions, including enhanced organizational agility, accelerated innovation cycles, and strengthened competitive positioning. This digital transformation becomes particularly critical in complex manufacturing environments facing regulatory pressures, sustainability mandates, and increasing product complexity, where the harmonious operation of these foundational systems determines market success and sustainable competitive advantage.
AI-Driven Strategic Modernization of Legacy ETL Workflows on Serverless Cloud Platforms
Legacy ETL systems create considerable operational overhead with manual capacity management, rigid scheduling models, and inadequate cloud integration support. The proposed framework in this article presents a systematic solution for migrating legacy ETL processes to serverless cloud environments, overcoming essential shortfalls through AI-aided workflow classification, natural language processing-assisted code translation, and event-oriented orchestration. Unlike previous lift-and-shift or containerized models with operational inflexibilities, this framework embraces fully managed serverless services integrated with machine learning functions. The result is considerable cost savings, performance enhancements via distributed processing engines, and improved data freshness. The framework involves automated inventory documentation via metadata-driven models, AI-aided complexity stratification across heterogeneous data, Apache Spark-based code refactoring using Business Process Model and Notation templates, and reliable, fault-tolerant, trigger-activated orchestrations. The model ensures improved reliability, scalability, and standards compliance while eliminating infrastructure-related operational issues. Challenges in metadata completeness gaps, translation processes with semantic drift, artificial intelligence-related bias, and operational constraints are addressed comprehensively through complete provenance tracking and parallel validations. The proposed framework outperforms current serverless cloud-migration solutions through intelligent automation and pattern-driven optimization. In an anonymized enterprise case study, the framework reduced end-to-end batch processing time and infrastructure cost while improving workflow success rates and migration throughput compared to the legacy environment. A formal evaluation section details workflow classification quality, translation accuracy, and operational improvements observed during the migration program.
Integrated Location–Allocation and Dynamic Routing for E-commerce Networks Under Demand Uncertainty
E-commerce logistics networks are under increasing pressure to strike a balance between operational efficiency and service quality under unstable patterns of demand. The classical planning models treat facility location and vehicle routing as a set of independent optimization problems, which do not reflect important interdependencies between strategic network design and operational dispatch decisions. These decision layers are combined in a two-stage stochastic programming framework with distribution facilities in place before demand is realized, and operational flexibility is retained by taking recourse actions of dynamic routing decisions. The former is the determination of facility activation and the initial demand zone assignments in the face of uncertainty, and the latter is the adaptive construction of vehicle routes as the customer orders become known. Rolling horizon heuristics allow path replanning within operating windows, which can handle real-time arrivals of orders without the need to add too much computational load. Geospatial road network data provides realistic estimates of distances that accommodate real driving routes and city topography. The validation of simulation has shown that the efficiency in transportation, cost reduction, and responsiveness of service are significantly improved as compared to traditional centralized or static planning strategies. The combined framework is specifically useful where third-party logistics providers, online grocery delivery businesses, and business-to-consumer retailers are dealing with uncertain demand and managing distributed customer bases with strong demand expectations in terms of delivery time.
Establishing Robust Data Governance Structures for Artificial Intelligence Deployment in Financial Institutions: A Compliance and Trust Perspective
The adoption of Artificial Intelligence in the banking industry has the potential to deliver significant benefits, but it also poses new challenges in terms of data governance. This article explores the importance of effective data governance frameworks for ensuring the integrity, security, and ethical use of data in AI implementations within the banking sector. It highlights the regulatory landscape, including the General Data Protection Regulation and the California Consumer Privacy Act, and the financial implications of non-compliance. The article discusses key principles of data governance for AI in banking, such as establishing clear policies, ensuring data quality, implementing access controls, and addressing data privacy and security concerns. It also emphasizes the importance of ethical considerations and the need for rigorous testing and monitoring of AI models. The article further examines best practices for integrating AI into existing data governance frameworks, including conducting risk assessments, establishing dedicated governance structures, defining roles and responsibilities, and investing in staff training. Finally, it underscores the importance of transparency and accountability in building trust among stakeholders and fostering a positive perception of AI adoption in the banking industry.
Neuro-Symbolic Enforcement Engines for Proactive Financial Crime Prevention
Financial organizations are faced with unprecedented challenges in identifying complex financial crimes that utilize AI, Deepfakes, and Multi-Level Obfuscations. Current compliance solutions are far from adequate by virtue of high levels of false positives and ineptness in spotting new forms of money crimes. Neuro-symbolic enforcement engines can be considered revolutionary solutions that seek to combine neural-based anomaly recognition and symbolic problem-solving capabilities for the proactive prevention of financial crimes. These novel solutions seek to combine transformer-based sequence models for temporal analysis of financial transactions with graph neural networks that represent regulatory policies as symbolic logic structures. These engines enable the system to recognize complex patterns in high-value financial transaction data as well as make rationalized decisions based on formalized compliance rules. Contrastive learning strategies can be used for improved identification of hidden criminal patterns in financial data by adequately addressing the high levels of class imbalance commonly found in Anti-Fraud analytics. Proactive predictive simulation for compliance outcomes on potentially criminal activity before escalation can be used for preemptive action plans. Generative models can be used for simulating new money crime scenarios for adversarial Validation. Real-time processing requirements for enforcement engines and satisfaction conditions for fairness on diverse customer sets can be considered as challenges for implementation.
Building Scalable User Interfaces for High-Demand Systems
Building scalable user interfaces for high-demand systems requires a comprehensive approach that integrates architectural design principles, data management strategies, asynchronous processing patterns, and adaptive optimization techniques. This article examines the critical factors that enable user interfaces to maintain peak performance under varying loads and user volumes, addressing the challenge of preventing the interface layer from becoming a system-wide bottleneck. Through analysis of component-based architectures, distributed state management, and RESTful design patterns, the article establishes foundational principles for creating interfaces inherently prepared for growth. The article explores efficient data handling mechanisms, including pagination, lazy loading, virtual scrolling, and differential rendering, that transform data-heavy interfaces into streamlined experiences. Asynchronous processing and non-blocking operations are examined as essential techniques for maintaining responsiveness during resource-intensive processes, with particular attention to event-driven architectures, web workers, and optimistic update patterns. Performance optimization strategies for variable load conditions are investigated, including multi-layer caching, resource prioritization, adaptive quality reduction, connection pooling, and request batching. By integrating empirical research findings with practical implementation approaches, this article provides a holistic framework for understanding how theoretical principles of human-computer interaction translate into tangible design decisions that support scalability, ensuring consistent user experiences whether serving small user groups or massive concurrent populations across diverse usage scenarios and fluctuating demand patterns.
Cloud-Native Infrastructure for K-12 Assessment Logistics: A Framework for Scalable Educational Systems
The modern environment of the K-12 standardized assessment logistics requires a technological infrastructure that can coordinate the complex operations of the various school districts and ensure high compliance. The emergence of cloud-native systems and microservices-based architectures has revolutionized systems that overcome the limitations of legacy systems, which had difficulty in scaling, reliability, and cost-effectiveness during peak testing times. The shift to monolithic, on-prem infrastructure to distributed, cloud-enabled systems can be seen as a radical rethink of the educational assessment logistics, which make resource allocation elastic, scale automatically, and provide greater operational agility. Current evaluation systems combine continuous integration and deployment pipelines, streamlined data processing frameworks, and advanced security architectures to provide dependable, agreeable, and efficient operationalization. Regulatory policies, such as FERPA and COPPA, offer a high degree of protection to student data, which requires a thorough security control, auditing policy, and vendor management procedures. Complex monitoring tools and chaos engineering methodologies enhance operational resilience by giving visibility in real time of system health, early anomalies, and certified and approved fault tolerance. The paradigms, as well as optimization methodologies and security frameworks, assist the educational assessment organizations in the provision of equitable, timely, and compliant assessment services at lowered operational overheads and infrastructure expenses. Such modernization initiatives may assist the larger educational objective of providing the student with fair opportunities to show learning by providing reliable, accessible, and high-quality assessment experiences.
Multi-Cloud Data Mesh Architectures: Integrating Governance and Interoperability via Autonomous Data Domains
Contemporary enterprises encounter substantial difficulties managing information dispersed across varied cloud infrastructures, geographically separated facilities, and specialized application environments. Traditional centralized frameworks, including consolidated data repositories and analytical warehouses, demonstrate limited capacity to deliver the required velocity, accuracy, and contextual intelligence necessary for sustained digital progression. Multi-Cloud Data Mesh constitutes a transformative architectural approach, advocating decentralized, domain-centric methodologies that systematically address intricate governance complexities and interoperability obstacles at the organizational scale. This framework establishes operational foundations through four fundamental tenets: Domain-Oriented Ownership, Data as a Product, Self-Serve Platform, and Federated Computational Governance. These architectural pillars collectively resolve decentralization imperatives, scalability prerequisites, interoperability complications, and sovereignty considerations inherent in modern enterprise ecosystems. Through ownership distribution to specialized domains, product-oriented information treatment, self-service platform provisioning, and federated governance implementation, organizations attain necessary scalability, operational flexibility, and contextual precision for continuous innovation across sophisticated multi-cloud landscapes
The Strategic Transformation Office: A Framework for Integrating C-Suite Advisory, M&A, and R&D Innovation in Global Engineering Services
Global Engineering and R&D service organizations face an enduring challenge: executive strategic visions rarely translate into operational results. This disconnect constrains performance across billion-dollar revenue segments. The Strategic Transformation Office framework provides a solution—a centralized mechanism that bridges boardroom aspirations with ground-level execution. Operating as an executive office extension, the STO unifies three domains: strategic account governance with C-suite advisory, complete M&A integration cycles, and R&D innovation management at scale. Through coordinated governance, deliberate execution strategies, and careful performance monitoring, this framework addresses the layered complexity of global service distribution. At the center, organizational ambidexterity enables businesses to improve existing processes while also creating future capacity by means of specialized institutions. Multiple elements determine success: sincere executive support, methodical talent acquisition, slow release strategies, and complex change management. Large engineering services firms wrestling with coordination tangles, integration obstacles, and innovation demands beyond traditional structures' capabilities will find this model especially relevant.
ERP-Powered ESG Intelligence: Measuring Carbon Footprint of Medical Device Supply Chains
There is a growing demand for environmental sustainability in the medical device industry. Globally, the healthcare systems' carbon emissions are largely attributable to complex supply chains, including raw materials extraction, product manufacture, distribution, and disposal. Today, ERP has evolved into sustainability intelligence software for monitoring and measuring environmental performance, enabling real-time carbon footprint tracking in procurement, manufacturing, and distribution. Modern ERP may deliver ESG modules for Scope 1, 2, and 3 emissions management, and help organizations meet quality management system regulations of medical device manufacturing. Artificial intelligence algorithms can increase the accuracy of emission factors and promote carbon accounting through the automatic classification of procurement data, classification of supplier features, and natural language processing of environmental documentation. Regulatory frameworks such as the EU Corporate Sustainability Reporting Directive (CSRD) and voluntary frameworks such as CDP climate disclosure are pressuring medtech companies to develop carbon accounting capability. ERP-enabled dashboards provide product-level carbon footprints, supplier engagement platforms, and hotspots for focused decarbonization activities while automating carbon accounting and reporting in compliance with global standards. Medical device companies adopting integrated ESG intelligence systems have a competitive advantage in regulatory compliance, operational efficiency, sustainability-linked funding, and distinguishing themselves in the sustainable healthcare market. By extending from retrospective environmental reporting to predictive carbon management, medical technology companies are positioned to lead healthcare, accelerate change, and meet growing stakeholder demand for climate transparency and assurance across global value chains.
Transforming Medication Order Processing Through Workflow Automation: A Scholarly Analysis of Large-Scale Pharmacy System Modernization
Current pharmacy practice is burdened with increasing prescription volumes, disparate clinical information, and emerging regulatory requirements that cannot be adequately addressed by current monolithic, batch-oriented medication order processing systems. This scholarly analysis examines how transforming medication order processing systems into intelligent, event-driven pharmacy workflows addresses critical challenges, including poor productivity in labor-intensive verification processes, disparate clinical data across information silos, and cognitive overload stemming from inefficient system architectures. Through the synthesis of published research, industry best practices, and architectural patterns documented in healthcare informatics literature, this work demonstrates how modern automated workflow architectures utilizing microservices decomposition, event-driven orchestration engines, and decoupled clinical decision logic enable real-time routing, parallel processing, self-adaptive workflows, and increased throughput velocity under stringent safety constraints. Evidence from diverse pharmacy implementation contexts reveals that workflow automation reduces manual review requirements by 40-50%, decreases verification turnaround times by 30-35%, and improves medication safety through consistent drug interaction screening and contraindication evaluation protocols. This framework uniquely integrates strangler pattern migration architecture with parallel clinical validation and real-time safety monitoring controls specifically tailored to pharmacy workflow constraints and regulatory requirements. The analysis establishes that successful modernization approaches incorporate incremental implementations following the strangler pattern, comprehensive user adoption strategies with purposeful change management, and multi-disciplinary governance frameworks focused on clinical appropriateness and regulatory compliance throughout the automation lifecycle. This work contributes to the healthcare informatics literature by providing an integrated architectural framework synthesizing event-driven computing paradigms, microservices design patterns, and clinical governance principles specifically tailored to pharmacy workflow modernization contexts.
Effect of rough walls on hydraulic jump in arectangular open channel and appreciate experimental and theoretical analysis
Hydraulic jumps have been attracted to many researchers for a long period of time because of its importance in designing hydraulic structures. Stilling basins are one of the possible solutions for hydraulic jump to dissipate kinetic energy to produce safe downstream flow, which causes no bed scour and bank erosion. Generally, the dimension of a hydraulic jump is taken as an indicator of the dimension of the stilling basin or as a design parameter. Lot of study has been made on hydraulic jump, and very limited studies have been reported in literature on the performance of hydraulic jumps formed in rectangular channel with rough walls, that way the paper is a valuable contribution to the subject. The purpose of the research work was to observe the phenomenon of the hydraulic jump,and to develop an understanding of the effect of roughness in an open horizontal channel. Also, to appreciate therelationship between the experimental results and the theoretical analysis based on the application of continuityand momentum principles. A series of experiments were carried out in a rectangular flume which consists of artificially roughenedwalls formed by circular balls with homogeneous and uniform shapes. The hydraulic jump parameters such as, pre jump depth, post jump depthand flow rate were measured for different roughness. During this study, sixty experimental runs were conducted by varying the Froude number, ranging from 2 to 17. It carries out on four sluice gate opening (h1 = 2, 3, 4 and 5 cm) with four different roughness elements of diameters (ε = 6, 8, 10 and 12mm) were introduced throughout the length of channel. The hydraulic jump considered in the present work have been controlled by sill with thirteen different sizes varying between 3 to 15 mm. The analysis of experimental data showed that the rough walls reduce the conjugate depthratiothan those on smooth walls. They have also, this parameter vary linearly with the Froude number and roughness where as other hydraulic jump characteristics are not. The analytical equation for the conjugate depthsratio as given byapplied theoretical the momentum equation, and itisadjusted well with the experimental data.Itis satisfactory for predicting the conjugate depth ratio of hydraulic jump on rough walls.
Digital Transformation of Supplier Management in Manufacturing: From Manual Processes to Cloud-Powered Collaboration
Contemporary supplier relationship management transformation signifies a pivotal advancement in manufacturing operations, confronting persistent obstacles in procurement efficacy, compliance oversight, and partnership collaboration. This article investigates cloud-based supplier collaboration platform deployment within the mining and building materials manufacturing domain, wherein enterprises administer vast supplier networks whilst managing intricate compliance mandates and operational limitations. Conventional manual methodologies encompassing documentation, spreadsheets, and dispersed communication mechanisms have chronically yielded protracted cycle durations, irregular data integrity, and constrained transparency throughout procurement divisions. The investigation scrutinizes how a prominent manufacturing enterprise revolutionized supplier management practices via integrated cloud technologies, intelligence-driven verification frameworks, and mechanized workflow structures. The inquiry evaluates deployment methodology, technological framework, and consequential organizational ramifications of this transformation endeavor. Discoveries reveal considerable enhancements in operational productivity, compliance execution, and supplier relationship caliber subsequent to platform adoption. The evaluation augments comprehension of how manufacturing enterprises can harness digital capabilities to strengthen supplier collaboration, diminish operational vulnerabilities, and establish resilient supply chain alliances. The article culminates in exploring wider ramifications for procurement transformation and delineating trajectories for sustainable digital supplier management practices within complex industrial settings.
Legacy Mainframe Application Modernization: Transformative Strategies and Organizational Outcomes
To keep pace in the rapidly changing business landscape, organizations are migrating their legacy mainframe applications, which are business-critical and developed decades ago in the COBOL, PL/I, and Assembler programming languages (on IBM mainframe computers, such as the IBM zSeries mainframe computer). While reliable and efficient, legacy architectures can impede an organization's ability to pivot and integrate into the digital economy. The journey to modernization presents various paths, including rehosting, replatforming, refactoring, and rewriting, each with its unique technical requirements, risk profile, and organizational implications. Financial services and insurance have the strongest evidence of success with new architectures. Improvements in the domains of transaction processing performance, infrastructure cost, and the ability to deliver new digital capabilities have been seen. New architectures also enable technologies, such as AI and machine learning, microservices, and omnichannel customer experiences, which were not possible with legacy architectures. Key success factors include strong executive sponsorship, technical and business stakeholder engagement, reverse engineering, and good governance that embraces the intrinsic complexity. In particular, challenges often arise in migrating data, rediscovering undocumented business logic, overcoming organizational inertia, and maintaining service continuity during the transformation. This will be done through a permanent set of modernization programs, cloud-native architectures, enterprise-grade automated migration tooling, and composable and flexible architectural design patterns. Organizations that succeed will reconcile the imperatives of innovation and operations and develop cultures that foster continuous learning and adaptation. Organizational capabilities in renewing and refreshing legacy systems have become essential for organizations to thrive and survive in the face of digital disruption and strengthening competition.
An Exploratory Spatial–Perceptual Framework for Heritage Revitalization: Mixed-Methods Evidence from Kota Lama Semarang
Research on urban heritage revitalization faces a critical methodological limitation: the lack of frameworks for statistically testing spatially varying relationships between physical transformation and human perception. This study bridges this gap by developing and validating an integrated Exploratory Spatial–Perceptual Framework (ESPF). The ESPF advances beyond descriptive integration by coupling computational sentiment analysis of geocoded reviews (using a fine-tuned BERT model, F1=0.89) with high-resolution spatial metrics of urban form, and subjecting their relationship to Geographically Weighted Regression (GWR). Applied to Kota Lama Semarang (2003–2024), the ESPF reveals that perceptual outcomes are not uniform but are conditioned by local context. GWR uncovered significant spatial heterogeneity: increased commercial density strongly boosted sentiment in the historic core (β = +0.42), yet eroded environmental perception in peripheral zones (β = -0.38). These divergent associations demonstrate a Contextual Tolerance Threshold, empirically validating a core, yet previously untested, principle of the Historic Urban Landscape (HUL) approach. The study's primary contribution is a replicable, evidence-based framework that transforms HUL from a conceptual model into an operational tool for diagnosing the spatially contingent impacts of heritage policy, offering a critical method for equitable revitalization in Global South cities.
Optimal Energy Management of a Microgrid Using Walrus Optimizer Algorithm
The economic operation of electrical systems is very crucial. Efficient energy management can lower operating costs, improve grid stability, and optimize resource allocation. This paper proposes a novel technique based on the walrus optimizer (WaO) algorithm for solving the optimal energy management (OEM) of a microgrid (MG) based on the IEEE 33-bus system topology. The investigated system incorporates several distributed energy resources (DERs), such as photovoltaic (PV) and wind turbine (WT) units, micro-turbine (MT), diesel generator (DG), and battery energy storage system (BESS). To assess the robustness of the proposed strategy, three separate pricing situations are simulated: stable, moderate volatility, and high volatility. The results show the algorithm's capacity to perform optimal energy arbitrage and peak shaving, ensuring power balance and lowering grid reliance during high-price periods.
GVIF: A Governed Vector Intelligence Framework for AI-Driven Cloud Data Modernization in Regulated Financial Systems
Financial institutions face unprecedented challenges from evolving fraud tactics, exploding data volumes, and increasingly stringent regulatory requirements that legacy batch-processing systems cannot adequately address. This article presents the Governed Vector Intelligence Framework (GVIF), a novel cloud-native architecture specifically designed by the author to transform financial data operations through three proprietary innovations: the Regulatory-Aligned Semantic Fabric (RASF), the Financial Vector Intelligence Core (FVIC), and the Human-Verified Adaptive Decisioning Loop (HVADL). Unlike conventional cloud modernization approaches that focus solely on infrastructure migration or basic data lake implementations, the author’s framework uniquely integrates semantic reasoning, vector-based pattern recognition, and human–AI collaborative governance directly into the processing architecture.The GVIF addresses critical industry gaps by enabling sub-second fraud detection with up to a 67% reduction in false positives, automating compliance documentation with up to a 73% reduction in compliance operating costs, and supporting real-time decisioning at sub-50 millisecond latency when compared with rule-based decision engines and batch-oriented ETL pipelines. Results are drawn from controlled pilot deployments and phased production implementations across payment processing, lending decisioning, and regulatory compliance operations, with metrics reflecting measured outcomes where instrumentation was available and conservative lower-bound estimates over defined evaluation windows. Industry deployments demonstrate quantified outcomes including average annual operational cost savings of approximately $4.2 million, an estimated 82% reduction in manual investigation workload, and a 58% decrease in fraud-related losses.The author’s contributions advance both national financial system resilience and institutional competitive positioning through scalable, audit-ready architectures that meet stringent regulatory requirements while preserving operational velocity. This document provides a repeatable, governance first approach to building a financial services organisation that will succeed in balancing AI driven innovation with regulatory compliance in a highly regulated, high stakes environment.
Securing the Data Gravity Well: A Zero-Trust Verification Framework for High-Frequency Data Replication from SAP Systems to Cloud-Native Data Warehouses
Enterprise migration from SAP systems to cloud-native data warehouses creates unprecedented security vulnerabilities as massive volumes of transactional data traverse high-frequency replication pipelines. Traditional perimeter-based defenses prove inadequate when sensitive financial records and customer information move beyond organizational boundaries into distributed cloud infrastructures. Current Change Data Capture mechanisms prioritize replication velocity over security, relying exclusively on transport-layer encryption that leaves data exposed to endpoint compromises, misconfigured storage, and insider threats. This article presents a comprehensive Zero-Trust verification framework specifically engineered for SAP-to-cloud data replication environments. The article fundamentally redesigns security architecture by embedding cryptographic controls directly into individual data payloads rather than depending on network-layer protections. Through payload-level encryption, SHA-256 cryptographic verification, and systematic identity propagation mechanisms, the article ensures data remains protected and verifiable throughout its journey from source systems to cloud warehouses. Experimental validation demonstrates that military-grade security need not sacrifice the near-real-time latency demands of modern analytics when combined with hardware acceleration and risk-proportional tiered controls. The article addresses critical compliance requirements for regulated industries by establishing immutable audit trails and preserving granular authorization contexts across heterogeneous platforms. Results confirm that organizations can successfully balance security imperatives with performance expectations, enabling secure cloud analytics without compromising operational velocity or data integrity guarantees essential for financial reporting and regulatory compliance.
A Compliance-Driven Framework for Data Curation and Gating in Machine Learning Training for Enterprise Privacy Infrastructure
Large-scale​‍​‌‍​‍‌​‍​‌‍​‍‌ machine learning systems spread over distributed infrastructures are confronted with crucial issues of managing sensitive data and, at the same time, abiding by regulatory requirements. In general, training pipelines do not have the means by which they can monitor the way in which protected data is introduced to model development; thus, there are quite significant privacy risks in decentralized environments. Also, the lack of complete visibility hinders the organizations' capability to trace data sources, grasp the movement of information between the systems, and check the conformity to the compliance requirements. In many cases, sensitive data is not properly safeguarded and is even allowed to be exploited beyond authorized purposes, both during training and inference stages. Automated classification systems detect sensitivity indicators within datasets and apply metadata tags specifying permissible uses at the precise moment information feeds into training operations. Gating mechanisms function as policy enforcement points that validate access requests against predefined rules, ensuring models access only data appropriate for declared purposes. Attribute-based access control looks at a variety of factors that include attributes of the subject, classes of the resources as well as certain conditions of the environment, and, based on all these factors, it dynamically makes the decision about the authorization. Machine learning anomaly detection is a kind of vigilant system that constantly watches the access patterns and, through behavioral analysis, it can pinpoint the variations from already established compliance standards. Distributed logging that is supported by blockchain keeps very detailed and at the same time very secure audit trails that enable, in the future, the checking of data usage throughout the lifecycle of the models.
Case study of Bou-saada City, A Integrating the Analytical Hierarchy Process (AHP) and GIS to assess threats from oasis urbanization. Case study of Bou-saada City, Algeria.
Urbanization's growing threat to oases' delicate and vital ecosystems, particularly in arid and semi-arid environments, demands immediate attention. These oases, essential to biodiversity, water resources, and indigenous inhabitants' means of subsistence in Algeria, are increasingly threatened by urbanization and climate change. To assess the threats caused by urbanization in oasis ecosystems while taking into account local characteristics and sustainability issues, a geographic information system (GIS) was combined with a hierarchical analytical process (AHP) to analyze several criteria, such as distance to agricultural areas, sands, settlements, valleys, and roads, land uses, and slope. The Analytical Hierarchy Process (AHP) was applied to determine the importance weights of each criterion. A simple additive weighting method was used to assess spatial suitability for urbanization within the study area. Each criterion was evaluated using AHP and mapped using GIS. The resulting land suitability was reported on a scale of 0 to 10, respectively, from least to most suitable sites for urbanization. The study revealed that physical factors significantly influence the deterioration of the oasis and have determined the direction and scale of urban development. As a result, 73.39 km2 of the study area is identified as the most suitable for urbanization, 91.64 km2 moderately suitable, and 50.51 km2 unsuitable.
Predictive Healthcare Analytics Using AI on Modernized Big Data Platforms: Transforming Clinical Outcomes and Operational Excellence
Recent transformations of healthcare analytics with artificial intelligence, machine learning, and modern big data have helped in guiding clinical decision-making, allocating resources, and improving clinical outcomes. Healthcare organizations are challenged with managing the rapid inflow of electronic health records, medical imaging, genomic sequencing, wearable technologies, and real-time patient monitoring devices, which require analytics infrastructures beyond what traditional systems can handle. Cloud-native architectures, distributed computing models, and scalable data stores enable the new generation of predictive analytics for anticipatory care models, which leverage cutting-edge artificial intelligence algorithms such as deep learning, natural language processing, and time-series analysis to extract insights from multi-dimensional and heterogeneous healthcare data and generate predictions of clinical deterioration, readmissions, and operational bottlenecks. Health systems show real-world implementations can reduce mortality, enhance intensive care unit capability and flow from the emergency department, and increase operating room capacity. Organizations with more advanced analytics capabilities and experience can achieve greater clinical impact, operational efficiencies, and cost reductions while remaining regulatory compliant and acting ethically. The ultimate vision for AI-enabled transformation in healthcare is a learning health system, in which clinical data continuously collected from the real world feed into predictive models to inform clinical decision-making across the individual patient population. Achieving this vision requires active cultural, cross-domain (clinical/technical/regulatory/ethical), and technological advancement.
The Transformation of Airline Distribution: From Legacy Documents to Dynamic Retail Models
The airline industry is undergoing a fundamental transformation in its distribution architecture, moving away from legacy document-based systems that have dominated commercial operations since the 1960s toward modern, offer-centric retail models. Traditional distribution relies on fragmented architectures that distribute commercial information across multiple independent documents including passenger name records, electronic tickets, and ancillary service documentation, creating operational inefficiencies and constraining commercial flexibility. This fragmentation limits airlines' ability to implement dynamic pricing, personalized offers, sophisticated bundling strategies, and real-time merchandising capabilities that are standard in contemporary digital commerce. The emerging paradigm introduces two core constructs: dynamic commercial propositions generated in real-time based on customer context and travel requirements, and unified commercial records that consolidate all transaction information into single authoritative sources throughout the customer journey. This transformation enables airlines to operate as sophisticated digital retailers capable of implementing continuous pricing strategies, customer-centric value optimization, and seamless omnichannel experiences. The shift represents more than technological modernization; it requires comprehensive organizational transformation including evolution of commercial teams from inventory managers to retail strategists, significant investment in analytical infrastructure and decision-support systems, and fundamental reconceptualization of revenue management from capacity-focused optimization to multidimensional value creation that balances immediate transaction revenue with long-term customer relationship development. Research demonstrates that modern distribution capabilities enable substantial improvements in operational efficiency, servicing simplification, merchandising effectiveness, and revenue capture through enhanced personalization, dynamic bundling, and ancillary service optimization across all distribution channels.
Autonomous Multi-Zone Replication for Zero-Loss Settlement Systems
Contemporary financial settlement infrastructures demand absolute data integrity across multi-zone and hybrid-cloud environments, where traditional replication strategies fail under real-world operational volatility. Zero-loss settlement systems—including national clearing networks, real-time payment rails, and high-frequency transactional platforms—face unprecedented challenges from network jitter, availability zone drift, asymmetric latency profiles, and heterogeneous infrastructure performance. Autonomous replication architectures emerge as the essential foundation for maintaining correctness guarantees in distributed financial ecosystems. These intelligent systems continuously optimize replication paths, enforce zero-loss commit governance, detect divergence through multidimensional consistency monitoring, and orchestrate sophisticated failover mechanisms. The convergence of software-defined networking paradigms, adaptive transport selection, and predictive analytics enables replication substrates to operate as self-optimizing systems aware of workload patterns, environmental risks, and latent failure conditions. Achieving external consistency at a global scale requires sophisticated timestamp management, distributed commit protocols, and dynamic adjustment of consistency levels based on application requirements. The architectural evolution toward autonomous replication represents a fundamental prerequisite for next-generation settlement infrastructure capable of operating at a planetary scale while preserving absolute correctness guarantees across expansive, heterogeneous operational environments.
Leveraging Large Language Models for Search Relevance Measurement in E-Commerce
Search relevance measurement represents a critical challenge in e-commerce systems, where the accuracy of query-item matching directly impacts user experience, conversion rates, and platform trust. Traditional approaches to measuring relevance have relied heavily on human annotation and behavioral signals, both of which present significant limitations in scalability, cost, and accuracy. Human evaluation suffers from inter-rater variability, limited coverage of long-tail queries, and prohibitive resource requirements when applied to large product catalogs. Implicit feedback signals such as clicks and conversions introduce noise through position bias and popularity effects, often failing to reflect true relevance. Recent advances in large language models offer a transformative alternative by leveraging semantic understanding, contextual reasoning, and world knowledge to assess query-item relationships at scale. Through careful prompt engineering, chain-of-thought reasoning, and validation against human-labeled datasets, these models can generate reliable relevance judgments that approximate or exceed human performance while covering vastly larger evaluation spaces. Implementation strategies, including teacher-student architectures, active learning for edge cases, and periodic human audits, enable organizations to balance accuracy with operational efficiency. This article addresses fundamental trade-offs between coverage, quality, and cost that have long constrained traditional relevance measurement methodologies, enabling more sophisticated search systems that genuinely understand and respond to user intent rather than merely optimizing for engagement metrics.
A Hybrid Monte Carlo–Machine Learning Framework for High-Energy Neutron Shielding Using Boron-Enhanced Concrete
Secondary neutrons produced by proton-target interactions in high-energy proton accelerator facilities present a major shielding challenge due to their high penetrability and broad energy spectra. In this study, neutron dose attenuation in B₄C- and FeB-enhanced concretes containing 5%, 10%, and 15% additives was investigated at a proton energy of 1000 MeV using FLUKA-based Monte Carlo (MC) simulations coupled with Machine-learning (ML) surrogate models.MC-generated dose data were used to train log-linear Linear Regression (log-linear LR), K-Nearest Neighbors (KNN), Random Forest (RF), and Gradient Boosting Regressor (GBR) models to enable rapid dose prediction. The results show that RF and GBR achieve the highest predictive accuracy under all configurations, with test-set R² values of approximately 0.98-0.99 in tunnel air and 0.99-0.996 in concrete shielding. In contrast, the LR model performs poorly in shielding regions due to strong nonlinearity, while KNN also provides high predictive accuracy exceeding 90%, albeit with lower performance compared to RF and GBR. A comparative analysis reveals that FeB-enhanced concrete exhibits more complex attenuation behavior due to the combined effects of iron-induced scattering and boron absorption. Overall, the validated hybrid MC-ML framework demonstrates that RF- and GBR-based surrogate models provide a fast, reliable, and computationally efficient approach for neutron dose estimation and shielding optimization in high-energy proton accelerator facilities.
Contextual Computing and AI Integration: Adaptive Decision Systems for Enterprise Environments
Artificial intelligence and contextual computing represent a paradigm shift, transforming enterprise systems from rigid, rule-based models to dynamic, context-driven decision-making platforms. By leveraging multidimensional contextual signals—including user roles, process timestamps, operational phases, system telemetry, and business constraints—AI-enabled systems deliver predictive analytics and automated control. The architectural foundation encompasses context signal taxonomies, feature engineering processes, temporal awareness structures, knowledge graphs, decision intelligence frameworks, and human-in-the-loop patterns. Recent advances emphasize multimodal representation learning, continual learning to address context drift, explainable AI, counterfactual reasoning, and privacy-preserving techniques such as federated learning. Enterprise applications spanning software development, telecommunications, aviation, and life sciences demonstrate value through risk-based testing, proactive service level agreement management, disruption recovery, and regulatory compliance. Implementation strategies address systematic signal identification, event-driven architectures, observability infrastructures, and privacy-by-design frameworks with comprehensive governance structures. Societal implications include workforce transformation, data privacy concerns, algorithmic bias mitigation, and accountability mechanisms. High-quality systems prioritize human-AI interaction through recommendation-first designs, explainable outputs, and systematic feedback loops that build trust while preserving human agency.
Can Small Teams Do MLOps Too? Starting Simple Without a Big Budget
MLOps is viewed as a complex process for the enterprise level, so MLOps can serve as a significant hindrance for small teams who want to apply machine learning operations. Yet, small teams can gain a tremendous advantage from MLOps by applying simplified, lean tooling, gradually moving toward more complex MLOps for their teams. In this article, a complete set is presented for small teams on how MLOps can be applied effectively for small teams without engaging cloud orchestration platforms. The article discusses ideas on how MLOps can be applied for small teams through basic version control for source and model files, tooling for simple experiments with file storage and databases, basic automation through shell scripts, basic MLOps tooling through system job schedulers, and basic MLOPs testing through standard testing results. MLOps can be made a successful process for small teams with the use of a progressive approach. According to the progressive approach, teams can move toward more complex MLOps concepts when their skills and resource availability increase. Therefore, in the progressive approach, automation investments can provide a remarkable difference for teams, meaning investment in MLOps can be avoided because automation can provide a negative effect for the team. Therefore, even the leanest teams can attain a solid basis for successful MLOps.
Architecting Enterprise to Cloud Connectivity for Mission Critical Applications
Mission-critical applications increasingly depend on cloud infrastructure, yet traditional connectivity approaches fail to meet demanding performance and resilience requirements. This paper's key contribution is a unified three-layer framework, Physical Diversity, Intelligent Control, and Operational Governance, that integrates quantified performance benchmarks for mission-critical enterprise-to-cloud connectivity. Unlike existing approaches that address physical redundancy, QoS, or monitoring independently, this framework explicitly models cross-layer dependencies and provides actionable guidance for achieving sub-second failover, differentiated service quality, and predictable multi-cloud operations. Analysis of representative enterprise deployments demonstrates 99.99% availability with 30-40% cost optimization through intelligent routing, alongside 50% MTTR reduction via cross-domain governance. The framework enables network architects to balance technical complexity, cost constraints, and operational readiness across hybrid cloud environments.
Quantum Computing Applications Across Industrial Sectors
Quantum computing serves as a revolutionary paradigm in computing, harnessing quantum mechanics principles such as quantum entanglement and superposition for optimization simulation, machine learning, and other problems that are a challenge to classical computer systems. The article will examine different usage opportunities in retail and supply chain operations, healthcare and life sciences domains, insurance operations, and financial service industries. Better computation can be achieved with quantum algorithms in discrete and continuous optimization, logistics network optimization, warehouse management, and product delivery optimized by quantum algorithms. Application domains in healthcare include simulation of molecules for medical breakthroughs, genomic analysis, and medical studies optimized by quantum eigenvalue algorithms and quantum eigensolver algorithms. The insurance industry portfolio targets catastrophic modeling, identification, and risk assessment optimized by quantum amplitude estimation algorithms and quantum machine learning algorithms. Financial service industries include derivative pricing, portfolio optimization, credit risk evaluation, and real-time risk calculation optimized in quantum algorithms. The path towards achieving quantum benefit in quantum technology involves using noisy intermediate quantum technology with hybrid quantum and classical designs and error management schemes, where integration opportunities for different industries arise from their common computation problem schemas in need of further scaling in hardware technology.
Quantum-Accelerated Schema Refactoring Engine for Predictive Cross-Cloud Data Optimization
Optimization of the database schema is a key challenge in modern enterprise data management systems, and it becomes more prominent in the context of cross-cloud migration projects, where the architecture of the older system often tends to cause performance issues and inefficient structures. This lends itself to a comprehensive solution strategy that integrates the computational models inspired by the power of quantum computing with the principles of artificial intelligence for the purpose of optimizing the complexity of the schema, removing anomalies based on structure, as well as the performance issues caused by the aging nature of the database system. The strategy encompasses the use of reinforcement learning techniques for optimizing the queries, error detection with the help of graph-based relation evaluation, structure exploration based on the principles of quantum annealing for finding the optimal structure modification, the utilization of prediction models based on the principles of neural networking for the forecasting of performance, and finally, the generation of specifications for the modification of the schema with the help of automatic program synthesis. The model also encompasses the provision for the integration of heterogeneous sources of data and the processing of queries with the consideration of error generation. This strategy reveals major advancements in the context of the varied dimensions of performance and enables the execution of schema optimization and migration with the help of reduced timelines and decreased incidence related to the generation of schema defects. The process also enables the provision of natural language specifications for the schema modification capabilities with decreased specialized knowledge, without any issues in the context of transformations.
Conversational AI Interface Transforming Complex Supply Chain Pricing Data into Actionable Insights for Warehouse Management
This article explores the transformative potential of large language models (LLMs) in enhancing supply chain pricing transparency through conversational interfaces. The article examines how AI-powered tools can convert complex pricing data into intuitive, natural language insights for sellers, particularly benefiting those without specialized analytics training. Through a comprehensive article incorporating domain-specific model training, robust data integration frameworks, and rigorous validation mechanisms, the article demonstrates significant improvements in decision-making efficiency and accuracy compared to traditional dashboard analytics. Implementation findings across diverse warehouse and distribution environments reveal enhanced seller trust, accelerated pricing decisions, and improved operational metrics. While identifying data integration challenges and model maintenance requirements, the article outlines promising expansions to additional supply chain functions and proposes a research roadmap for future development focusing on multimodal integration, temporal reasoning capabilities, and increased system autonomy. The article contributes valuable insights into how conversational AI can democratize pricing intelligence within supply chain management while offering a path toward more transparent, efficient, and adaptive pricing ecosystems.
Efficient Ingestion, Labeling, and Storage of LiDAR, Radar, and Camera Datasets: Optimizing Storage Formats for Autonomous Vehicle Development
The development of autonomous vehicle technologies has placed new standards of managing the multimodal sensor data that includes LiDAR point clouds, radar measurements, and camera images. The contemporary autonomous platforms produce large volumes of non-uniform perception data that need advanced infrastructure to ingest, label, and store in the long term. Combining different modalities of sensing creates significant technical issues throughout the data lifetime, both during the initial capture and archival preservation. Infrastructure Edge preprocessing hardware will need to synchronize heterogeneous sensor streams with less than millisecond accuracy and apply real-time compression to achieve a lower bandwidth in transmission. Automated labeling workflows based on pre-trained perception models can help reduce the manual annotation load by a significant factor, and label quality is likely to be high enough to be used in training. Storage format optimization trades off conflicting demands such as compression performance, random access performance, and compatibility with the distributed processing model. The use of cloud-scale deployment allows managing the petabyte-scale volumes of data and optimizing the costs of infrastructure based on the tiered storage approaches that match the storage performance properties with the access patterns. Benchmark datasets have played a vital role in achieving advances in autonomous vehicle perception, and have allowed the performance to be systematically compared and the outstanding challenges to be reflected. Direct neural network processing of point cloud data has permitted significant progress in performance in detection and segmentation. Further progress will be realised through further growing multi-modal datasets with multi-modes of operation and persistent advances in algorithms that fuse sensor data and build scene understanding.
API-First Modernization and Applied AI in Insurance Digital Transformation: A Multi-Year Enterprise Case Study
A comprehensive two-year initiative transformed digital self-servicing capabilities at a major United States insurance organization through the implementation of a unified API-first infrastructure combined with strategic artificial intelligence deployment. The progression from disparate legacy architectures exhibiting inconsistent customer interaction points culminated in the establishment of a high-performance intelligent digital servicing infrastructure serving millions of policyholders. Core emphasis centered on constructing contemporary microservices and API infrastructure capable of synthesizing multiple foundational legacy platforms while delivering rapid response characteristics and standardizing servicing experiences across web, mobile, and partner channels. The infrastructure became the operational automation foundation, facilitating AI-driven analytical capabilities, document intelligence processing, and personalized servicing workflows. Measurable advancement in customer experience metrics, engineering velocity indicators, and operational efficiency parameters resulted, establishing groundwork for intelligent predictive insurance servicing powered by machine learning technologies. This article analyzes architectural determinations, implementation sequences, and quantifiable results of enterprise-wide transformation, providing perspectives for organizations pursuing comparable modernization initiatives.
Intelligent RF Planning and Performance Optimization for Carrier Aggregation and Dual Connectivity in 5G-Advanced Networks
Next-generation wireless networks leverage Carrier Aggregation (CA) and Dual Connectivity (DC) to deliver enhanced throughput and spectral efficiency by simultaneously utilizing multiple frequency bands. However, coordinating FR1 sub-6 GHz coverage with FR2 mmWave capacity presents significant challenges in resource allocation, inter-band handover management, and QoS maintenance under dynamic traffic conditions. This article proposes an intelligent framework combining predictive algorithms for inter-band handover with reinforcement learning-based load balancing optimization. The framework integrates deterministic propagation models with AI-driven traffic forecasting to enable proactive resource allocation across aggregated carriers. Performance evaluation demonstrates substantial improvements in handover success rates, throughput consistency, and resource utilization compared to conventional rule-based methods. Results from urban and suburban deployment scenarios validate the effectiveness of machine learning models in predicting mobility patterns and optimizing carrier selection. The proposed solution addresses critical gaps in multi-band network planning while ensuring latency requirements and service continuity during CA/DC transitions, providing practical insights for 5G-Advanced and early 6G network deployments.
A Hybrid Medical Image Registration Framework Integrating Fiducial Markers and Wavelet-Based Mutual Information
This paper proposes a hybrid medical image registration framework aimed at improving the alignment accuracy of multimodal brain MRI images. The proposed approach integrates the geometric robustness of fiducial markers with the multi-resolution frequency analysis capability of the Daubechies wavelet transform (db2). Initially, three artificial circular fiducial markers are placed at stable anatomical landmarks and automatically detected using the Circular Hough Transform with radii ranging from 6 to 20 pixels, enabling reliable estimation of an initial affine transformation and reducing gross alignment errors. Subsequently, a two-dimensional Discrete Wavelet Transform (DWT) is applied to the reference and moving images, where the low-frequency (LL) sub-bands are exploited to perform fine registration using Mutual Information (MI) as the similarity metric. This frequency-domain refinement enhances robustness against noise and intensity variations. Experimental evaluations on healthy and tumor-affected brain MRI datasets demonstrate that the proposed hybrid framework outperforms conventional intensity-based methods relying on MSE, SSIM, and PSNR, particularly in challenging pathological scenarios.
Demystifying Distributed Microservices Architecture for Enterprise-Scale Systems
Microservices are the contemporary approach to designing and developing enterprise software. Modern microservices architectures can support never-before-seen scalability while providing an incredible amount of flexibility to meet the varying demands of businesses today. Microservices provide a better solution through modular design. Independent services work together to deliver complete business capabilities. Many engineers struggle when transitioning from monolithic systems. This article breaks down the fundamental concepts behind microservices. Service decomposition and bounded contexts create effective boundaries between components. Stateless design allows systems to scale horizontally with ease. Asynchronous messaging removes tight coupling between services. API contracts maintain reliable integration across independent teams. Cloud-native platforms deliver the orchestration that distributed systems need. A microservice observability tool allows developers to easily identify, report, and understand many aspects of each other’s microservices, as well as their various interrelationships. Consequently, a single failed microservice does not result in cascading failures throughout an entire application. Data management techniques handle consistency in decentralized environments. DevOps automation enables teams to deploy continuously without risk. Organizations can build resilient platforms by applying these microservices principles correctly. The concepts explained here help practitioners implement distributed systems successfully. Real-world patterns guide teams through common challenges. Understanding these fundamentals allows organizations to meet enterprise demands effectively.
Enterprise Integration Modernization with SAP BTP: A Strategic Framework for Hybrid and Multi-Cloud Architectures
Hybrid and multi-cloud adoption has created architectural fragmentation, operational inconsistency, and integration complexity across enterprise landscapes. Modern organizations require unified integration capabilities that support distributed workloads, decoupled communication models, and environment-agnostic connectivity. SAP Business Technology Platform (BTP) enables such modernization by providing foundational services for API governance, cloud-native integration, event-driven communication, and lifecycle automation. This framework addresses the need to handle heterogeneous platforms, mitigate legacy middleware limitations, and ensure standardized governance across cloud and on-premise systems. SAP BTP establishes a central connectivity fabric linking multi-cloud services such as Azure, Amazon Web Services (AWS), and Google Cloud Platform (GCP) with enterprise applications through consolidated policies and automated pipelines. A multi-layer architectural model encompasses integration foundation, cloud automation, optimization intelligence, and lifecycle governance components. Implementation in a global manufacturing organization demonstrated tangible improvements, including reduced integration change lead time, increased API adoption, and decreased operational incidents. The resulting modernization outcomes include reduced technical debt, improved integration agility, strengthened governance consistency, and enhanced observability for distributed operations across hybrid enterprise environments.
Modern Table Formats for Data Lakehouse Architectures: A Comprehensive Analysis of Apache Iceberg, Delta Lake, and Apache Hudi
The transformation of enterprise data infrastructure has necessitated the creation of sophisticated table formats bridging the gap between traditional data lakes and data warehouses. Apache Iceberg, Delta Lake, and Apache Hudi have emerged as revolutionary technologies providing ACID transactional semantics, schema evolution, and advanced metadata management over cloud object storage systems. These formats address fundamental constraints of traditional data lake systems by delivering database-grade reliability without sacrificing cost-effectiveness and scalability of distributed storage. Each format embodies distinct architectural philosophies: Iceberg emphasizes engine neutrality with scalable metadata hierarchies, Delta Lake focuses on deep Apache Spark integration with optimized analytical query performance, and Hudi specializes in streaming ingestion patterns with efficient change data capture support. The architectural foundations include hierarchical metadata structures, transaction log mechanisms, and timeline-based state tracking, each presenting trade-offs in scalability, consistency, and operational complexity. Schema evolution capabilities enable structural adaptation without data rewrites, while sophisticated update and delete mechanisms using Copy-On-Write and Merge-On-Read strategies optimize for diverse workload characteristics. Streaming integration features facilitate real-time analytics through incremental query interfaces, native Kafka integration, and unified batch-streaming processing paradigms. However, these established formats encounter inherent overhead when handling true real-time workloads with millisecond-level latency requirements. Emerging technologies such as Apache Fluss and Apache Paimon represent next-generation solutions specifically architected for real-time data lake use cases, addressing limitations in existing frameworks through streaming-native architectures, unified streaming-batch storage engines, and optimized real-time query processing capabilities. Query optimization techniques, including hidden partitioning, data skipping, Z-order clustering, and comprehensive indexing subsystems, provide significant performance improvements for analytical workloads. The selection of appropriate table formats constitutes a foundational architectural decision with lasting implications for platform agility, operational complexity, and analytical capabilities, requiring careful evaluation of workload patterns, real-time requirements, ecosystem constraints, and strategic technology directions.
Real-Time Visibility and Intelligent Orchestration in Healthcare Supply Chain Management: An Architectural Framework
The healthcare supply chain management industry is also facing the issue of visibility despite investing heavily in enterprise resource planning and tracking technologies. Essentially, the issue lies in the lack of overall situational awareness rather than in the collection of information. Medical institutions are facing shortages of life-critical stock while the same item is expiring in the stores. Healthcare professionals also spend a considerable part of their work schedule looking for devices rather than attending to the patients. Traditional point solutions do not solve the specific issue but are all isolated. Existing systems collect data without interpretation and report historical states rather than enabling prospective intervention. The article presents a comprehensive architectural framework for transitioning healthcare operations from static tracking to intelligent orchestration. The proposed framework comprises four interconnected processing layers, including physical signal acquisition through miniaturized sensors, intelligent capture mechanisms utilizing existing infrastructure, artificial intelligence interpretation through cloud-based machine learning, and purpose-built clinical applications delivering actionable workflows. The tool tackles precision in asset management, inventory optimization, and condition monitoring within pharmaceutical supply chain networks. Real-time visibility helps healthcare systems transition from reactive firefighting to predictive optimization. Clinical settings need systems that can constantly analyze information and take action on their own, instead of just gathering more data.
Shandilya Avadhanam Venkata Krishna Sastry
EmbedGuard: Cross-Layer Detection and Provenance Attestation for Adversarial Embedding Attacks in RAG Systems
Embedding-based Retrieval-Augmented Generation (RAG) systems are critical infrastructure for production AI applications, yet they remain vulnerable to embedding space poisoning attacks that achieve disproportionate success with minimal payloads (<1% corpus contamination, resulting in>80% attack success rates). Current single-layer defense approaches optimize for high-amplitude signals in narrow-dimensional subspaces, making them systematically vulnerable to coordinated cross-layer attacks that distribute adversarial signals across architectural layers. EmbedGuard is an adaptive, cross-layer detection framework integrating hardware-backed cryptographic attestation with statistical anomaly detection across four RAG architectural layers: prompt layer injection detection, embedding layer hardware attestation via Trusted Execution Environments (TEEs), retrieval layer distributional analysis, and output layer consistency verification. The framework employs efficient techniques, including incremental Principal Component Analysis and Kullback-Leibler divergence metrics, to detect subtle, coordinated attacks while maintaining production-grade latencies. Evaluation of a production-scale system (500,000 embeddings, 47,000 queries) demonstrates a 94.7% detection rate for optimization-based attacks and 89.3% for adaptive attacks, with a 3.2% false positive rate and a 51ms mean latency overhead. Ablation studies quantify an 18.4 percentage point improvement from cross-layer correlation over the best single-layer approach. The framework operates in three deployment modes—passive logging, gated human review, and active automatic remediation—enabling deployment across diverse organizational contexts and security requirements while protecting against adversarial embedding manipulation.
Image Processing, Bitrate Optimization, and Mobile Upload Efficiency
Media transmission efficiency is a growingly important issue in the modern mobile ecosystem as device imaging capabilities grow significantly beyond the network infrastructure limitations. High megapixel sensors and high frame rate video capture on Smartphones create large files of media that often surpass realistic transmission capabilities in cellular and wireless network settings. The use of advanced compression algorithms, smart bitrate control, and adaptive encoding schemes allows for reducing the file size significantly without compromising the visual quality that is not noticeable to human viewers in a wide range of content types. The format conversion, chroma subsampling, adaptive transcoding, and asynchronous processing architecture all minimize bandwidth usage and speed up the completion of media transmission under limited network conditions. Client-side processing deployment is a strategic distribution of computational load to mobile devices, which reduces the requirements of server infrastructure and improves user experience by reducing the transmission time. Quality evaluation systems based on a combination of complementary perceptual measures inform the choice of optimization parameters, which ensures uniform visual fidelity to a wide range of content and heterogeneous device platforms. New codec technologies, such as VVC and AV1, are expected to achieve significant efficiency gains to enable smooth media exchange at scale in resource-limited mobile computing devices.
Tool Use and External System Integration in Conversational AI: From Function Calling to Autonomous Agents
Conversational AI agents have evolved from text-based agents into tool-using agents that can perform computation and physical actions. This builds on the capabilities of pre-trained language models by integrating them into tool-using systems, performing accurate calculations, and supporting multi-agent systems, sometimes in conjunction with human operators and sometimes independently. They all follow a similar pattern of using self-supervised learning to allow agents to self-invoke and control external tools, verifying tool execution using reasoning models, using hierarchical recovery to counteract tool-using system failures, and accessing thousands of real-world APIs using semantic retrieval mechanisms, as well as a wide variety of protocol standards. Compositional emergence, a subset of emergent behavior, is the idea that complex behaviors can emerge in a system composed of simple components engaging in pipelining and parallelism. Examples include tree-based deliberative strategies that converge on a solution with efficient exploration, world model planning, micro- to meta-level error recovery, and graceful degradation in production contexts. Multi-agent orchestration mechanisms allow modular components to communicate via a declarative requirement and event-driven message systems. Limitations in the scalability, hallucination, and explainability of these systems need to be addressed to create effective multi-agent architectures. Future work could research online reinforcement learning, quantum planning algorithms, and representational robotics that transfer knowledge of tool use from simulations to real-world applications.
Digital Transformation of Aviation Supply Chains: An SAP-Based Control Tower Implementation Framework
The aviation industry faces persistent challenges in supply chain management due to high volatility, regulatory complexity, and critical dependencies on timely part availability. Aircraft on Ground (AOG) situations demand immediate responsiveness, yet legacy fragmented systems create operational blind spots and reactive decision-making patterns. This case demonstrates the design and deployment of a Supply Chain Control Tower at Allegiant Air, built on SAP Business Technology Platform, integrating S/4HANA, Ariba Network, and Analytics Cloud. The solution establishes unified visibility across procurement, logistics, and inventory operations through event-driven automation and machine learning-powered forecasting capabilities. Implementation followed a phased agile framework spanning discovery, development, pilot deployment, and performance evaluation. The Control Tower enables proactive alerts for shipment delays and material shortages, transforming operational paradigms from reactive firefighting to predictive intervention. Results demonstrate substantial improvements in procurement cycle efficiency, supplier collaboration quality, and operational downtime reduction. The implementation provides a validated architectural blueprint and replicable framework for aviation organizations transitioning toward integrated, intelligent supply chain operations.
Driving Industry 4.0 Maturity in Retail Manufacturing: A Case Study on IoT, Cloud, and RPA Integration
Retail manufacturing faces unique operational challenges, including high-volume production requirements, perishability constraints, compressed profit margins, and comprehensive traceability mandates that distinguish it from traditional discrete manufacturing sectors. This article examines systematic Industry 4.0 transformation across greenfield dairy processing and case-ready beef manufacturing facilities through multi-year longitudinal case documentation. The transformation centered on enterprise resource planning systems as authoritative data sources while extending capabilities through Internet of Things sensor networks, cloud analytics platforms, mobile applications, and robotic process automation. Implementation utilized Scaled Agile Framework principles with product-oriented teams organized around manufacturing capabilities rather than application-centric structures. The architectural framework emphasized clean-core principles, maintaining system upgradeability and regulatory compliance while enabling innovation through carefully controlled extensions. Results demonstrate substantial improvements in operational equipment efficiency, supply chain performance, process accuracy, and administrative efficiency. Strategic outcomes include validated reusable templates enabling accelerated facility rollouts, risk reduction in subsequent implementations, and architecture reusability across diverse product lines. Critical success factors encompass enterprise resource planning-anchored architecture with edge extension, early investment in data contracts and integration standards, product-oriented delivery methodology adoption, and perishables-specific design considerations. The article establishes a comprehensive framework for retail manufacturing digital transformation that balances operational stability with innovation requirements while maintaining regulatory compliance and long-term system sustainability.
AI-Driven Network Automation in Cloud Environments
The integration of artificial intelligence into cloud network automation represents a fundamental transformation in infrastructure management, enabling organizations to transition from manual, error-prone processes to intelligent, self-optimizing systems. This article explores how AI-driven network automation leverages natural language processing, large language models, and multi-region orchestration platforms to revolutionize network deployment, policy management, and operational efficiency in cloud environments. The article examines the convergence of machine learning operations and development operations methodologies, demonstrating how predictive analytics capabilities enhance continuous integration and deployment pipelines while enabling dynamic resource allocation and proactive system optimization. Through comprehensive analysis of implementation architectures, validation frameworks, and operational transformations, this article reveals that AI-powered automation dramatically compresses deployment timelines from weeks to hours, enhances policy lifecycle management through continuous evaluation, and enables self-healing capabilities that detect and remediate issues before impacting service availability. The investigation also addresses critical challenges, including model accuracy assurance, governance framework implementation, and the necessary cultural and skills transformation required for successful adoption. By examining the technical enhancements, validation methodologies, and predictive capabilities of AI-driven systems, this research provides insights into how organizations can harness intelligent automation to achieve substantial improvements in deployment velocity, system reliability, and resource optimization while maintaining appropriate human oversight and risk controls for mission-critical infrastructure operations.
Workflow Optimization in SAP-Centered Enterprise Systems Through Process-First Architecture
Enterprise Resource Planning Systems (ERP) are built upon SAP, allowing for standardized and automated processes for finance, procurement, sales, inventory, and payment functions throughout the enterprise. Advanced technology exists today to provide businesses with multiple workflow process optimization options, but many organizations still have too much inefficiency in their operations and processes. As a result, many organizations add automation solutions to their processes without first identifying their current workflows. Process-first architecture offers an alternative perspective that prioritizes workflow understanding before technical implementation. End-to-end business workflows must be documented before system configuration and integration activities begin. Misaligned assumptions create operational bottlenecks that persist throughout the system lifecycle. Fragmented process ownership leads to unclear accountability and inconsistent execution. Incomplete workflow mapping prevents effective system design and implementation. Manual interventions accumulate when systems fail to match operational reality. Recurring exceptions indicate fundamental disconnects between system design and business needs. A structured framework enables organizations to analyze enterprise workflows systematically. Identifying breakpoints helps pinpoint where workflows fail under normal conditions. Aligning system design with actual operational behavior improves outcomes significantly. Process-first architecture enhances system adoption by matching user expectations and operational patterns. Rework decreases when workflows function as designed from implementation forward. Scalability improves in SAP-centered landscapes that maintain workflow clarity over time. Governance practices sustain optimized workflows and support continuous improvement initiatives. Organizations benefit from reduced processing delays and improved operational efficiency.
AI in Smart Manufacturing: Driving Efficiency and Sustainability
Global manufacturing sectors are undergoing unprecedented change through artificial intelligence assimilation, radically redesigning production models and operational excellence. Digital twin technologies come forth as pillar innovations that establish harmonized virtual duplicates of physical manufacturing assets, as well as predictive maintenance approaches, significantly prolonging equipment working lifespans. Machine learning models exhibit stunning aptitude in optimizing production processes by using deep learning models for the examination of huge streams of sensor data and enforcing real-time parameter control in various manufacturing settings. Convolutional neural community-based totally computer vision systems remodel nice manage operations with tremendous disorder detection accuracy costs, even as ensuring uniform inspection performance amid continuous production runs. Sophisticated predictive protection deployments utilize high-degree algorithms to examine vibration styles, temperature variances, and acoustic signatures so that one can facilitate proactive intervention schemes that avert catastrophic device breakdowns. Sustainability programs gain significantly from smart resource management platforms that provide optimal energy consumption patterns and reduce material wastage through circular economy concepts. The article analyzes extensive uses of artificial intelligence in manufacturing fields, emphasizing advancements in automated defect detection, real-time process control, and the reduction of environmental impact. Transformation in industries in the direction of clever manufacturing showcases quantitative gains in operational effectiveness, first-rate control, and environmentally friendly production methods, putting new requirements for competitiveness in international markets.
Assessment of groundwater quality and its suitability for drinking in the ouargla region northeastern Algerian Sahara
This study focused on the determination of the different physicochemical characteristics of groundwater from 59 boreholes distributed over the three main aquifers of the Ouargla region (South-East of Algeria). The quality of the water was also assessed in order to evaluate their amenities and the international standards of drinking water quality of the World Health Organization (WHO) and the water quality index (WQI). The physicochemical parameters selected for this study, namely pH, total dissolved solids, electrical conductivity, total hardness, alkalinity, calcium, magnesium, sodium, potassium, bicarbonate, chloride, sulfate, and Temperature, were determined by following the procedures prescribed by international standards. were determined by following the procedures prescribed by international standards. In groundwater quality studies, statistical analysis techniques such as correlation analysis, principal component analysis (PCA) and analysis of variance (ANOVA) were used to assess the main factors and mechanisms governing spatial variations in the Ouargla basin region. The results obtained showed that the facies characterizing the study area were a mixed combination of Ca–Mg–Cl, Ca-Cl and Na-Cl, with the dominant ions being most often chlorides and sulfates, as well as calcium and magnesium. The WQI values ​​calculated for the Ouargla basin region ranged from 174.69 to 312.71. The geospatial distribution of the water quality index shows that the water of the Albian aquifer is poor, while the majority of the water of the Miopliocene and Senonian aquifers is very poor. However, the results of this study indicate that most of the water is not potable and requires additional pre-treatment before consumption.
Genetic diversity and population structure of Saharan bread wheat (Triticum aestivum L.) landraces from Algeria revealed by SSR markers
Bread wheat (Triticum aestivum L.) cultivation in the Algerian Sahara relies on traditional landraces that have evolved to withstand extreme hyper-arid environmental conditions. These local populations represent a vital yet under-utilized genetic resource for modern breeding programs aiming to improve drought and heat tolerance. This study investigated the genetic diversity and population structure of 16 local landraces collected from the Adrar and Tamanrasset regions in southern Algeria. Genotyping was performed using 13 polymorphic simple sequence repeat (SSR) markers selected for their reliable genome coverage. Genetic diversity parameters were estimated using standard metrics, while population structure was analyzed using Bayesian clustering approaches and Principal Coordinate Analysis (PCoA). Results revealed a total of 72 alleles across the studied loci, with a mean of 5.54 alleles per locus. The genetic diversity indices indicated a moderate level of diversity, with a mean polymorphic information content (PIC) of 0.57, a mean expected heterozygosity (He) of 0.573, and a mean observed heterozygosity (Ho) of 0.271. Analysis of Molecular Variance (AMOVA) revealed that the majority of genetic variation resided among individuals (56%) and within individuals (32%), with a significant 12% attributed to genetic differentiation between the two geographic populations. Bayesian STRUCTURE analysis and PCoA consistently identified two distinct genetic pools that largely corresponded to the geographic origin of the accessions, although some admixture was observed. Overall, the results demonstrate substantial genetic variability and a clear structured population pattern within Saharan bread wheat. These landraces represent a valuable genetic resource for conservation strategies and for future research and breeding efforts targeting wheat improvement under water-limited environments.
For larger software projects, it is increasingly important that builds are visible and auditable. While GNU Make can be used for both legacy and modern projects, it has historically offered limited visibility of command executions, dependency evaluations, and file system accesses. Additionally, this inhibits debugging, reproducible builds, and optimization for improved performance. This article presents a complete rethink of GNU Make based upon an improved meta-log system. This enables us to transform it from a rule executor to a build engine with rich observability of its inner workings and performance. Information in the meta-log provides observability across several axes: command executions with full arguments, working directories (for path resolution), state transitions of dependencies (with change indicators), captured output streams, and detailed analysis of use of the file system via system-call monitoring. The modular architecture allows for separation of command logging, dependency state tracking, and behavioral file access analysis. Their respective observability capabilities can be enabled or disabled as needed for diagnostics or performance considerations. The tool configuration allows a range of observability/overhead, including disabling globally in production builds, targeting specific source files with detailed tracing, and masking the program behavior of non-target programs. The tool has produced substantial benefit in practice, substantially reducing the required proof effort and having stable performance. The tool has also detected bugs with little training. Taking cues from distributed data processing systems, an augmented meta-log can offer modern build-level requirements like reproducibility across machines, distributed build coordination, and smart automation. Implementation studies show that while logging all operations adds important overhead for I/O-heavy workloads, the log's size can be considerably reduced through smart instrumentation without sacrificing its diagnostic value. Such modernization brings GNU Make up to parity with other modern build systems while keeping the original intent of being lightweight and extensible, which has kept it in use for the last 40 years of software engineering development.
Architectural Decoupling of Enterprise Planning Platforms: A Framework for Multi-Entity Organizations
Enterprise architecture in multi-entity organizations has a permanent structural problem: embedded planning capabilities in transactional systems prevent scaling and flexibility across the organization. Another architectural approach separates global planning as a distinct enterprise capability from transactional execution platforms, such as enterprise resource planning (ERP) systems. A layered architecture allows for cross-entity uniform logic. Transactional platforms develop separately to meet local, regulatory, or organizational needs, and the architectural split helps firms cope with continuous structural and regulatory change. The well-designed decoupled architecture consists of three layers. The planning layer includes enterprise decision-making capabilities such as demand forecasting, supply planning, and financial alignment. The execution layer handles the transactional systems, specifically optimized to process transactions in high volumes. The integration layer consists of data transformation, master data management and data governance components. The predominant integration layer patterns are batch extract-transform-load and event-driven architectures. Canonical data models abstract specific system data representation to data entities relevant to the enterprise. Governance frameworks define approval gates for projects based on scope or cost. Methodological standardization ensures that operational and financial plans are aligned to baselines and, once implemented, that cross-functional forums are used to resolve definitional issues whilst the organizational structure evolves.
Scalable Cloud Data Warehousing: Architectural Trends, Challenges, and Future Directions
Enterprise data platforms continue facing significant scalability constraints as information volumes grow exponentially across diverse formats. Traditional data warehousing architectures built on tightly coupled storage and compute layers fail to deliver adequate elasticity for contemporary analytical demands. Resource underutilization during off-peak periods remains substantial. Performance degradation during demand surges presents ongoing operational challenges. The Unified Elastic Governance Architecture presented in the current article addresses persistent enterprise difficulties through integrated design principles. Decoupled storage-compute configurations enable independent scaling of persistent data layers and ephemeral processing resources. Metadata-driven automation mechanisms reduce manual operational interventions across pipeline orchestration and schema management functions. Policy-based governance enforcement ensures consistent compliance postures across heterogeneous platform deployments. Workload isolation through multi-cluster configurations eliminates resource contention between concurrent user communities. Predictive scaling models anticipate demand patterns before actual workload materialization. Cross-platform policy synchronization maintains semantic equivalence despite platform-specific implementation variations. Unified audit aggregation simplifies compliance verification processes across distributed environments. The architectural framework delivers measurable improvements across query throughput, resource utilization efficiency, cost predictability, and governance consistency dimensions. Enterprise implementations benefit from coordinated optimization addressing compute elasticity, automation capabilities, and compliance requirements as interdependent concerns rather than isolated objectives.
Comparing Globally Distributed Databases and Traditional Relational Databases: An Architectural Analysis
Globally-distributed database designs differ from customary RDBMS designs. Trade-offs in multi-region operational capacity, disaster recovery, and long-term operational viability can lead to both conflicting and complementary planned priorities. Globally-distributed databases make use of synchronous replication protocols, automated failover procedures, and consensus-based consistency models to provide strong availability guarantees across physical and geographic boundaries. However, these designs still suffer from the intrinsic latency and performance tradeoffs of cross-region coordination, while relational database architectures like PostgreSQL are able to achieve superior local performance characteristics and cost efficiency when workloads are localized to regions. That said, they require explicit operational documentation and orchestration for cross-region disaster recovery and failover. Beyond technical considerations, the architectural choices also include differing assumptions of system complexity: is it centralized within the database platform or distributed among operational processes and organizational practices? Organizational preference for a high base cost with automated resilience or for a low base cost with flexibility and explicit DR responsibilities affects the choice of architecture. An organization's decision depends on the requirement for geographic distribution, the tolerance for downtime and access latency, the cost of the infrastructure, and the availability of operational expertise.
Adaptive Zero Trust Security: Integrating Artificial Intelligence for Dynamic Cloud Security
Zero Trust Architecture(ZTA) has become a widely adopted security approach for modern cloud and hybrid systems, built on the principle that no user, device, or service should be trusted by default. While this model has improved security compared with traditional perimeter-based methods, it still faces multiple challenges. They frequently depend on static access policies, tightly coupled identity and access management systems, and complex integration across heterogeneous platforms. As organizations expand into multi-cloud environments, edge computing, and highly distributed workloads, these limitations make it difficult for ZTA to keep pace with real-world complexity. This article presents a conceptual view on the evolution of Zero Trust beyond static policy enforcement. The article highlights why current architectures struggle to adapt changes in user behavior, workload context, and threat conditions. Artificial Intelligence can play a critical role in strengthening Zero Trust by helping systems interpret behavioral signals, understand patterns, and adjust policies more dynamically. Instead of treating Zero Trust as a fixed architecture, this perspective frames it as a continuously adapting trust model supported by AI-driven insights. This article outlines a path toward more resilient, context-aware, and scalable security in cloud environments.
Architecting Cloud-Native Risk Analytics Platforms for Enterprise-Scale Decision Intelligence
Enterprise risk management has evolved from static compliance reporting toward continuous intelligence-driven decision support systems that address contemporary challenges including climate volatility, catastrophic loss exposure, and regulatory complexity. Traditional monolithic risk management systems demonstrate fundamental architectural limitations when confronting modern enterprise demands, particularly in processing heterogeneous data sources and maintaining real-time responsiveness. Cloud-native risk analytics platforms emerge as strategic solutions through integration-first design principles that enable seamless connectivity with diverse data sources while maintaining operational excellence. This article establishes comprehensive architectural guidelines for cloud-native risk platforms based on empirical implementation evidence across multiple enterprise environments. The content addresses critical design considerations for systems processing geospatial, climatic, and corporate datasets while maintaining zero-downtime availability, analytical accuracy, and enterprise trust through embedded governance mechanisms. Performance optimization strategies encompass environmental sustainability considerations alongside computational efficiency through systematic elimination of redundant processing operations and advanced algorithmic implementations. Microservices architecture enables precise resource management through independent service scaling, horizontal scaling patterns, and intelligent orchestration capabilities that support enterprise-scale operations. Container orchestration platforms provide foundational infrastructure for event-driven scheduling and zero-downtime modernization strategies. The article demonstrates that successful cloud-native risk platforms integrate scalability requirements with sustainability considerations and business continuity needs while creating sustainable competitive advantages through enhanced risk intelligence and operational efficiency.
Practice-Driven Enterprise DevOps: Risk Management, Scalability, Automation, and Prevention
Enterprise systems on a global scale need orchestrated innovation in deployment automation, risk governance, infrastructure optimization, compliance, and incident prevention and prediction. Continuous operational experimentation and measurement, not stand-alone theoretical experimentation, drives this type of innovation in high-reliability organizations. This article presents an integrated set of frameworks for five cross-domain dimensions of enterprise DevOps maturity: multidimensional release risk quantification for graduated approval workflows with the level of approval proportional to the risk of deployment; scalability optimization to avoid non-linear performance degradation over critical infrastructure cost; policy-as-code compliance automation to transition regulatory verification from periodic audits to always-on system properties; organizational knowledge transfer so that capabilities are efficiently distributed across remote teams; and failure pattern taxonomy development to inform architecture designs that avoid failure. Experience with large interbank financial systems shows how systematic adoption of these integrated frameworks supports faster delivery flows, operational reliability, regulatory compliance, and efficient capability diffusion. Organizations that successfully use end-to-end optimization across all five dimensions can achieve meaningful improvements in DevOps maturity and performance.
Advanced Customer Return Process using SAP EWM-SD Integration
The integration of SAP Extended Warehouse Management with SAP Sales and Distribution systems is a crucial feature of modern logistics companies. Constant technical connectivity eliminates manual data transfers, reducing errors and accelerating disposition decisions. The framework provides real-time visibility of the return operations, including the creation of return orders and the ultimate disposition of inventory, which fosters a feeling of transparency that improves operational efficiency and customer experiences. Standardized processes guarantee uniformity in the networks of facilities, and mobile technologies provide data capture and validation. The data of returns are transformed into predictive intelligence through advanced analytics to support proactive resource planning and strategic supply chain decisions. The incorporation of quality inspection improves the disposition determination by ensuring that goods are transported effectively to the appropriate places, such as restocking, refurbishing, or recycling. Automation of financial reconciliation ensures the consistency of the master data between physical inventory records and accounting systems to facilitate proper reporting and compliance. Sustainability-oriented models incorporate environmental aspects into the process of returns, which contributes to the initiatives of the circular economy. Continuous improvement methods allow incremental efficiency improvements to compound over time, creating competitive advantages. Fraud detection secures company assets and preserves the integrity of a return program. Companies that invest in a comprehensive SAP EWM-SD integration have quantifiable gains in terms of operational efficiency, financial performance, customer satisfaction, and environmental responsibility.
Closed-Loop Hallucination Mitigation in Generative Language Systems Through Adaptive Retrieval, Multi-Source Verification, and Judge-Guided Feedback
Generative language technologies have experienced remarkable transformation over recent years, evolving from laboratory prototypes into production-ready infrastructure serving enterprise analytics, strategic decision frameworks, and automated information services. Technical maturity notwithstanding, these platforms persistently generate linguistically sophisticated text lacking verifiable factual foundations—termed hallucinations within technical discourse. This phenomenon introduces considerable operational hazards across sensitive application contexts, particularly analytics transformation projects, automated documentation workflows, article compilation activities, and strategic advisory operations, wherein factually incorrect outputs may propagate through organizational systems without triggering detection mechanisms. Contemporary control strategies predominantly implement post-generation validation procedures or utilize static document retrieval architectures, addressing observable manifestations while fundamental causative factors remain unresolved. The architectural methodology introduced here reconceptualizes factual precision as an actively maintained operational characteristic rather than a discrete validation checkpoint executed after content generation. Synthesizing adaptive document retrieval procedures, multifaceted assertion assessment protocols, inter-source concordance analysis, and evaluation-guided regeneration mechanisms, this architectural framework facilitates continuous operational self-correction. The article transforms epistemic uncertainty from concealed system deficiency into an explicit, communicable system attribute, thereby enabling informed user interpretation. This design philosophy establishes that effective hallucination control necessitates integrated architectural planning rather than supplementary filtering layers, enabling trustworthy deployment across enterprise and research operational contexts.
Navigating Generative AI Integration: A Framework for Educational Transformation
The integration of Generative Artificial Intelligence into higher education is a transformative inflection point and requires comprehensive institutional re-conceptualization across pedagogy, communication, assessment, and governance. Large Language Models have shifted from being on the periphery of all tasks to being central, in which the processes through which knowledge is accessed, synthesized, and demonstrated fundamentally reframe everything. Educational institutions must both harness the affordances of AI to drive personalization and efficiency without sacrificing academic integrity and related essential cognitive skills development. Intelligent Tutoring Systems facilitate adaptive learning pathways, where standardized models are replaced with responsive frameworks that address the individual needs of all students through continuous content adjustment and predictive analytics. The automation of faculty workload frees up instructional time for mentoring and innovative pedagogy, yet it also introduces new literacy requirements centered on algorithmic communication competencies. The rise of prompt engineering as a foundational skill refashions writing from the making of content to the directing of content and critical curation. Traditional methodologies for assessment are collapsing in the face of AI's generative capabilities, demanding a migration toward authentic evaluation that centers on real-world applications, oral examination formats, and process-based learning verification. Governance frameworks that were built for predictable systems are being shown as inadequate for managing autonomous, opaque, computational systems (with emergent properties). An effective institutional response requires risk-stratified categorization systems, algorithmic transparency protocols, and human-in-the-loop accountability to ensure that technological development bolsters, rather than cripple, education's mission.
Engineering-Driven Cloud Cost Optimization at Enterprise Scale: An Applied Success Story with Measured Outcomes in a Large Healthcare Enterprise
Enterprises continue to experience rising cloud infrastructure costs as application portfolios expand and cloud‑native architectures proliferate. This paper presents a production‑validated, engineering‑driven cloud cost optimization framework implemented at a large U.S. healthcare enterprise operating a multi‑account, multi‑region platform with unpredictable demand, strict compliance requirements, and high‑availability expectations. The framework integrates utilization‑based rightsizing, demand‑aware autoscaling, storage lifecycle management, commitment‑based pricing, and continuous governance through policy‑as‑code, explicit cost ownership, and cost–performance observability, embedding cost optimization into routine operations via automated enforcement and recurring review checkpoints. Over two fiscal years, the initiative reduced annualized infrastructure cost by approximately 18% in year one and an additional 5% in year two, despite continued growth in platform demand and overall spend, while availability, latency, and error rates remained within established service‑level objectives. The results demonstrate that cloud cost efficiency can be operationalized as a continuous engineering discipline – complementing site reliability engineering practices – rather than treated as an episodic financial exercise, and provide a repeatable, scalable model for enterprises seeking measurable and sustainable optimization.
Differentiating Fitness Brands through Female-Focused Holistic Training Models: Implications for Market Share Growth
The fitness industry is increasingly characterized by intense competition and shifting consumer expectations that extend beyond physical training outcomes toward holistic wellbeing. This study examines how female-focused holistic training models contribute to fitness brand differentiation and influence market share growth. Using a mixed-methods research design, the study integrates consumer-level survey data with managerial insights to analyze the relationships among holistic training attributes, perceived brand differentiation, and market performance indicators. The results reveal that holistic dimensions—particularly mental wellness integration, recovery-oriented programming, and community engagement—significantly enhance brand differentiation. Mediation analysis demonstrates that brand differentiation plays a central role in translating holistic training models into improved customer acquisition, retention, referral intensity, and perceived market share growth. Comparative analysis further shows that female-focused holistic fitness brands outperform conventional brands, especially in retention-led and advocacy-driven growth metrics. The findings underscore that sustainable competitive advantage in the fitness industry is increasingly driven by emotionally resonant, relationship-centered, and wellbeing-oriented value propositions. This study offers strategic insights for fitness brands seeking long-term growth through inclusive, holistic, and female-centric training models.
Advanced Patterns in Distributed Event-Driven Architecture: Multi-Channel Communication and Workflow Orchestration for High-Volume Enterprise Systems
Event-driven architecture is a common architectural pattern for solving problems in enterprise systems that need to be responsive, scalable, and effective at handling live streaming feeds of events across distributed system nodes. This article describes architectural patterns to solve the most critical problems in multi-channel WebSocket systems, subscription-based event filtering, stateful workflow orchestration, and resource-constrained state replication. Multi-channel WebSocket architectures allow events of different types to be emitted on separate channels, leading to more flexible application control over data flow and independent scaling. By providing filtering at the server side, subscription-based filtering methods invert the broadcast model, potentially improving battery and bandwidth consumption. Lightweight state management patterns enable the processing of high-frequency events with low computational and memory costs. Business Process Management integration patterns enable resilient orchestration of complex and long-running multi-step processes through durable state persistence and compensation transactions. Retriable connection management patterns include graceful reconnection, exponential backoff, and explicit state synchronization as strategies to handle network disconnections. They have been shown to improve battery life, state management, and short- and long-term uptime benefits when implemented in production systems serving millions of concurrent users and connected devices.
AI in Supply Chain Transportation: Optimizing Costs Through Predictive Analytics
Transportation inefficiencies within supply chains impose substantial hidden costs that ultimately inflate consumer prices at retail. This article examines how artificial intelligence applications address these inefficiencies through predictive analytics and optimization algorithms across multiple operational dimensions. The article explores AI-driven solutions for demand forecasting, shortage prediction, arrival time estimation, and warehouse resource management, analyzing their collective impact on transportation cost structures. Using a combination of case studies and real data, the article shows how predictive abilities help companies allocate resources more effectively, which lowers costs related to extra labor, crowded warehouses, delays with trailers, and poor choices in carrier selection. The article reveals that organizations implementing AI-powered transportation management systems achieve meaningful operational improvements and cost reductions that flow through to final retail pricing equations. While implementation challenges, including data quality requirements, system integration complexity, and organizational change management, present obstacles, successful adopters realize sustained competitive advantages. The article establishes that AI technologies have evolved from experimental innovations to essential capabilities for supply chain competitiveness. This article contributes to the theoretical understanding of cost pass-through mechanisms in retail pricing while providing practical insights for supply chain managers, retailers, and policymakers navigating digital transformation. The article identifies future research directions, including holistic supply chain integration, advanced algorithmic techniques, sustainability considerations, and scalability across diverse organizational contexts.
Investigating the Security and Privacy Implications of Cloud-Based Disaster Recovery
The article explores security weaknesses in cloud disaster recovery setups. One of the major concerns is that confidential information may be exposed unintentionally during replication. The article also highlights that if the recovery environment's access control is not robust, it can be a great exploit for hackers to gain unauthorized access. Furthermore, the privacy aspect is affected due to data residency and multi-tenant isolation issues. In the same article, it is pointed out that protection such as encryption, identity management, and network security controls is are way in which the system can be shielded against threats. The next paragraph talks about the need for having proper governance in place as a prerequisite for deploying security recovery architectures. Apart from that, regular reviews of the system, execution of data handling measures, and preparation for the response team in the event of an incident are also part of the organization’s playbook. The last sentence states that companies should not forget the challenge of striking a balance between the need to recover operations and protect data so as not to lose their resilience, and at the same time not break the rules.
AI-Enabled Network-Level Credit Risk Navigator (NCRN): Risk Propagation Paths for Systemic Vulnerability in Digital Lending Platforms
Digital lending platforms have achieved remarkable success in borrower-level risk assessment through sophisticated machine learning models, yet traditional portfolio monitoring remains fundamentally reactive and overlooks systemic vulnerabilities that emerge from network-level interdependencies. Current credit risk frameworks treat borrowers as independent entities and rely on lagging aggregate indicators, creating critical blind spots in detecting correlated defaults and systemic risk propagation. The Network-Level Credit Risk Navigator (NCRN) addresses these limitations by modeling digital lending ecosystems as dynamic, heterogeneous networks where borrowers, lenders, products, and economic factors form complex webs of interdependency. NCRN integrates graph neural networks for learning network-aware representations, contagion simulation engines for modeling distress propagation, and anomaly detection systems for identifying emerging vulnerabilities. The framework introduces Risk Propagation Paths as directed routes through the network that quantify specific transmission mechanisms for financial distress under various stress scenarios. Through comprehensive validation using synthetic datasets and historical backtesting, NCRN demonstrates the ability to detect systemic risk clusters months earlier than conventional delinquency-based monitoring systems. The implementation framework addresses practical challenges, including entity resolution at scale, real-time graph maintenance, computational optimization through sampling and hierarchical modeling, and integration with existing risk management workflows. NCRN transforms credit risk oversight from reactive portfolio monitoring to proactive network-level vulnerability detection, enabling digital lenders to identify and mitigate systemic risks before they manifest as portfolio-wide losses.
Optimizing Time-to-First-Ad in Mobile Advertising Systems
The article on Time-to-First-Ad (TTFA) optimization in mobile advertising platforms emphasizes how the speed of delivery of an advertisement is vital to the success of a business in the competitive online market. The article discusses the technical strategies that are multifaceted and are used by the current platforms to ensure that latency is minimized when a cold start situation is experienced, where system resources are the most limited. The article provides a comprehensive discussion of the progressive delivery mechanism, predictive prefetching techniques, edge computing systems, and systematic latency budgeting habits through the analysis of which the latest generations of leading platforms have remodeled their delivery architecture to attain significant performance gains. The discourse is not restricted to the technical implementations, but also includes the organizational aspects of the matter, such as dedicated performance engineering teams and advanced observability systems that allow ongoing optimization. The article shows a positive relationship between TTFA improvements in user engagement metrics, more impression volume, and higher revenue generation capabilities. With the advertising technology industry still moving forward, the article puts latency optimization as a strategic necessity as opposed to a technical one, and the effect of its application cuts across the entire advertising ecosystem, between publishers and final consumers.
Advancing Healthcare EDI Interoperability Through Informatica Cloud B2B Gateway Quality Engineering
Healthcare EDI operations face persistent quality challenges that undermine transaction success rates and revenue cycle performance. Partner onboarding delays, schema validation failures, mapping errors, and acknowledgment reconciliation gaps create operational inefficiencies. This paper presents a practitioner-oriented Quality Engineering framework specifically designed for Informatica Cloud B2B Gateway implementations. The framework delivers four reusable components: a validation taxonomy covering structural, semantic, code-set, and payer-specific rules; a partner onboarding test pack reducing integration timelines; an observability framework defining EDI-specific KPIs; and compliance-as-code approaches for HIPAA requirements. A detailed case study demonstrates framework application to 837 Professional claims processing, showing rejection rate reduction from 8.2% to 2.1%, resubmission turnaround improvement from 14 days to 3 days, and 340% first-year ROI. Organizations implementing these practices achieve measurable improvements in transaction success rates, operational efficiency, financial performance, and regulatory compliance. The framework provides healthcare IT teams with concrete, deployable components for immediate value realization.
AI-Driven Anomaly Detection Models for Preventing Claims Denials and Revenue Leakage in Healthcare
Despite the increased use of new automation technologies, healthcare organizations continue to see declining revenues due to increased billing discrepancies and claims denials. The traditional use of rule-based validation frameworks does not allow for the effective identification of new patterns of denials or the resolution of complicated coding inconsistencies. Artificial Intelligence (AI) and Machine Learning (ML) technologies provide the ability to significantly improve the detection of anomalies related to either claims and/or revenue before they impact adjudication results. Feature engineering creates contextualized inputs that improve model precision and support compliance requirements. Through this integration, organizations are able to strengthen the accuracy of their claims and minimize the financial impact of revenue loss associated with claim denials. Unsupervised learning techniques discover unknown patterns without requiring labeled training data. Supervised models predict denial probability based on historical adjudication outcomes. Natural language processing analyzes unstructured documentation to identify inconsistencies and gaps. By incorporating and integrating anomaly detection software into the Quality Engineering Pipeline, organizations should be able to detect anomalies in real-time and continuously improve their overall operational accuracy. By adhering to applicable HIPAA regulations and developing ethical governance frameworks for their AI models, organizations have an opportunity to achieve significant cost savings related to preventable denials and manual interventions. First-pass payment accuracy improves substantially while reimbursement cycles accelerate. Future advancements include generative AI for synthetic testing, self-correcting mapping engines, and collaborative human-AI validation systems. AI-powered Quality Engineering represents the future of healthcare claims automation and operational excellence.
Effect Of The Combined Addition Of Brick Waste Powder And Dune Sand Powder On The Mechanical Performance Of A Lime-Treated Gypsiferous Tuff
This study investigates the improvement of mechanical performance and water durability of a gypsiferous tuff from southeastern Algeria, with the aim of its application in road infrastructure. The natural material, characterized by low cohesion and high water sensitivity, was treated through the combined addition of lime, finely ground brick waste powder, and dune sand powder. Several mix formulations were developed to assess the individual and synergistic effects of these additions on the mechanical behavior of the tuff. The experimental program included unconfined compressive strength, indirect tensile strength, water sensitivity, durability under wetting–drying cycles, and direct shear strength parameters. The results indicate that lime treatment significantly enhances material cohesion, while the incorporation of brick waste powder leads to a substantial mechanical gain attributed to the development of pozzolanic reactions. The contribution of dune sand powder is mainly associated with a granular densification effect, resulting in an improvement of the internal friction angle and overall structural stability. Microstructural analysis using scanning electron microscopy corroborates these macroscopic findings by revealing the formation of secondary cementitious products and a marked reduction in porosity for the most efficient mixtures.
Elastic Riemannian Framework for Whole-to-Part Shape Registration
In this paper, we focus on one of partial shape analysis tasks, by proposing a solution for the whole-to-part shape registration problem using a Riemannian Framework extended from past works. We have a part of a planar curve considered as open one, and the goal is to extract an open curve segment from another closed curve that gives the best alignment by minimizing the Riemannian shape distance between the given part and all parts of the closed curve with the same length. Our contribution here is: (1) extracting all curves with same length of the target curve from the second by changing the starting point, (2) for each part extracted, find the best registration using an elastic Riemannian metric and calculating the shape distance between them (3) the minimum of these distances define the distance desired and the curve is the part which gives this distance. Finally, to evaluate the performance of this algorithm, the results are shown in application to shapes from MPEG-7 dataset.
Graph-Based Duplicate Trade Detection and Idempotency Framework Implementation in Distributed Electronic Trading Systems
To prevent the reprocessing of the same trade message in different distributed financial infrastructures, electronic trading systems must have powerful duplicate trade detection protocols. Redundant messages are a result of network timeouts, TCP retransmission protocols, upstream retry queues, and manual resubmission workflows that are part of heterogeneous trading structures. Idempotency models define message uniqueness by using composite business keys, cryptographic fingerprints using the SHA-256 hashing functions, and deduplication logic on time windows that trades off between accuracy of detection and scalability of computation. Graphed graph frameworks are enhanced with blockchain and deliver distributed data models to specify intricate trade relations in the form of immutable ledger records, smart contract validation logic, and multi-channel designs, which assure information integrity across trading networks. Multi-channel correlation algorithms differentiate between actual trade amendments and replay events based on machine learning classification models and partial fill cases and cross-venue execution strategies. Strategies of implementation are used to optimize parameters of tolerance windows with the use of hierarchical composite key matching, progressive sampled indexing, and container-based pre-fetching strategies. Microsecond-latency duplicate-detection In-memory caching architectures in conjunction with Bloom filter probabilistic structures can achieve duplicate detection at millions of trade messages per day to protect downstream risk management and regulatory reporting systems against position inflation and compliance violations.
Quantum Security Risks in Autonomous Vehicles and Smart Transportation Systems
Self-driving cars and intelligent transport networks face unprecedented security challenges from quantum computing capabilities. Quantum algorithms can efficiently solve mathematical problems underlying current cryptographic architectures that protect vehicular communications, authentication, and software integrity. This article analyzes quantum-specific threat vectors including harvest-now-decrypt-later attacks, authentication forgery enabling safety-critical message spoofing, and long-term integrity attacks on over-the-air updates. Technical solutions include post-quantum cryptographic algorithms, hybrid security models, and crypto-agile architectures. The lifecycle threat model addresses temporal aspects of data sensitivity for vehicles with long operational lifetimes. Policy implications encompass national security, economic competitiveness, regulatory frameworks, and international standards coordination. Quantum-resilient transportation security requires urgent, coordinated efforts from industry, government, and standards organizations.
Semantic-Aware Neural Query Optimization: Bridging the Gap Between Distributed Frameworks and Large Language Models at Terabyte Scale
While processing and managing datasets of the same order of terabytes is commonplace in any business environment, distributed SQL engines such as Apache Spark SQL, Trino, etc., have been proliferating. Despite their advanced Cost-Based Optimizers (CBOs) and adaptive execution plans, the underlying statistical heuristics have turned out to be less useful in the presence of large-scale datasets, high-dimensional data, and data skew. On the architectural side, the NP-Hard join order problem, the cost of shuffle, and the fragility of the pipeline execution model in MPP systems are considered. It is observed that learn-based optimizers in documentation and records, such as Neo, Bao, and LITHE, along with customary and pre-existing learned optimization strategies used today in industrial systems, lack semantic reasoning capabilities to optimize queries through rewriting and physically planning hints. To address this, the Semantic-Aware Neural Query Optimization (SANQO) framework is proposed. SANQO integrates Large Language Models (LLMs) as a supervisory agent within the database kernel, utilizing a novel retrieve-then-rewrite paradigm to inject precise execution hints (e.g., broadcast thresholds, skew salting) and perform structural rewrites that elude static evaluation. Through a rigorous comparative evaluation with related work, this article demonstrates that SANQO offers a plausible, strictly superior pathway for optimizing distributed workloads, transforming the database from a passive execution engine into an active, reasoning system.
Infrastructure-Level Intelligence: Embedding AI into Data Movement and Validation Layers
Data volumes and complexity push enterprise and governance systems beyond their design assumptions. Rule-based data validation techniques can no longer appropriately model modern data ecosystems. Infrastructure-Level Intelligence (ILI) refers to applying AI to data movement and validation infrastructure, rather than application-level data validation. ILI aims to help quickly spot problems, adjust data validation rules on the fly, fix data movement automatically, improve data quality as needed, and provide other features by processing data close to where The implementations of such compliance-driven use cases have led to huge improvements in these areas with dramatic reductions of data quality issues, faster validation times, more accurate processes, and lower numbers of manual reconciliations. Customary static architectures have failed to accommodate changing data behaviors, regulations, and increasing interdependencies between systems. Adopting real-time intelligent infrastructure allows error detection, dynamic logic, and resilience, which are particularly useful in areas with compliance needs. Infrastructure artificial intelligence will be an essential part of future national regulatory systems, helping to solve problems related to data accuracy and compliance that traditional methods couldn't handle.
Continuous-Learning Recommendation Engines: Fusing Deep Metric Embeddings with Contextual Bandits at Web Scale
A novel recommendation system architecture integrates deep metric-learning embeddings with contextual-bandit exploration to address limitations of traditional recommenders. This hybrid design enables personalized content delivery while continuously adapting to evolving user preferences and contexts. The architecture captures semantic relationships between items through high-dimensional embeddings while systematically exploring new options through contextual bandits, creating a balanced approach to the exploitation-exploration dilemma. Implementation features include efficient feature pipeline orchestration, on-device inference capabilities, and robust counterfactual evaluation techniques. Both offline and online evaluations demonstrate significant improvements in click-through rates, cold-start adaptation speed, and recommendation diversity without sacrificing relevance. Responsible deployment patterns including shadow mode operation, fairness audits, and feedback loop dampening ensure the system functions ethically at web scale.
Neutralizing "State-Drift" in Distributed Retail: The Mechanics of Global Event Cascading
State-Drift in distributed retail systems is a problem of critical concern where digital representations of business processes become spatially disconnected with physical reality in geographically distributed associate devices and transaction endpoints. A national-scale retail business needs architectural designs that have the capability to persist in coherent operational states and yet allow the inherent limitations of distributed computing, such as network latency, intermittent connectivity, and simultaneous multi-actor interactions. The technical basis of neutralising state drift is the Event-Driven Microservices architecture, actually Event-Carried State Transfer, which offers the technical opportunity to carry the entire state context on event payloads, permitting edge devices to exist in a non-failed state during network partitions. Essential for the Stateful Business Process Orchestration of Global Event Cascading makes coordinating a single source of truth across regional clusters of devices possible without repetitive associate interventions at the cost of operational efficiency. Combining globally distributed NoSQL platforms with Change Feed mechanisms changes the formerly static databases into live event streams, which drive real-time downstream processing. Edge computing architectures reduce latency through placing the resources of computation nearer to the data sources, and AI-based infrastructure triage provides the ability to self-heal in order to decrease the mean time to recovery. Such architectural patterns are not only limited to retail but also to financial services and other read-only infrastructure systems that need real-time state synchronisation and regulatory adherence.
Self-Healing Telecom Networks with AI-Driven Autonomous Operations (AIOps)
The current telecommunications infrastructure faces unparalleled operational complexity with the increasing proliferation of fifth-generation wireless systems, software-defined architectures, and distributed edge computing deployments over global networks. Traditional manual approaches to network management are insufficient to address issues of scale, velocity, and complexity in modern communication systems, where cascading failures lead to rapid service degradation impacting millions of subscribers. This article attempts to analyze the enabling approaches of Artificial Intelligence (AI)-driven autonomous operations of self-healing telecommunication networks that are capable of detecting anomalies, diagnosing root causes, and executing corrective actions without human involvement. This article discusses machine learning techniques for fault detection and prediction, autonomous remediation frameworks using reinforcement learning, and intent-based networking; intelligent algorithms for resource allocation and traffic engineering; and critical implementation issues regarding data quality, model reliability, and security vulnerabilities. Emerging technologies such as edge intelligence architectures, deep reinforcement learning, and distributed computing frameworks for intelligent networks present future directions that may offer enhanced functionality to overcome limitations. AI-powered self-healing networks fundamentally change telecommunications operations from the current reactive maintenance paradigms to proactive and predictive management solutions to optimally manage service quality, operational efficiency, and infrastructure utilization for increasingly complex network environments.
A Dynamic Management System of Synchronous-Asynchronous API Services
Modern applications increasingly rely on data from heterogeneous APIs, databases, and IoT sources, yet existing integration solutions struggle with latency, schema heterogeneity, data consistency, and fault tolerance. This paper proposes a hybrid synchronous-asynchronous API management system that enables low-latency interactive access and high-throughput background processing. The core innovation is automatic, schema-agnostic ingestion of JSON responses into endpoint-specific Redis tables, augmented with reception timestamps, enabling efficient time-series and historical querying without manual ETL or schema inference. The system exposes a REST/WebSocket interface for synchronous requests, a Kafka-based asynchronous pipeline for complex aggregations, and a SQL-like query layer over the stored data. Evaluation on a Kubernetes cluster with simulated heterogeneous APIs demonstrates median response times of 78 ms for synchronous aggregations (35–62% faster than GraphQL federation and REST aggregation baselines) and 99.2% data completeness under 30% concurrent source failure in asynchronous mode. Case studies in smart-city dashboards and healthcare monitoring illustrate reduced development effort and improved performance. The architecture bridges API consumption and database functionality, offering a lightweight alternative for real-time federated analysis and IoT integration.
PCIe and CXL Interconnects for AI Accelerators: Performance, Latency, and Telemetry
The exponential growth of artificial intelligence and high-performance computing workloads has fundamentally transformed system design priorities, shifting performance bottlenecks from computational resources to interconnect infrastructure. Modern AI accelerators demand unprecedented bandwidth and predictable latency characteristics that challenge traditional interconnect technologies, particularly in heterogeneous computing environments where processors, accelerators, and memory expansion devices must communicate efficiently across complex fabric topologies. This article presents a unified framework for characterizing and optimizing PCIe 6.0 and CXL 3.0 interconnect fabrics, addressing critical challenges in latency predictability, throughput maximization, and operational observability. Through comprehensive modeling of protocol stack behaviors, physical layer characteristics, and multi-level switching architectures, the article quantifies end-to-end latency contributors including forward error correction overhead, credit-based flow control delays, and switch traversal costs. A telemetry-driven runtime framework integrates PCIe Advanced Error Reporting and CXL Fabric Manager interfaces to enable adaptive optimization policies encompassing credit-aware scheduling, dynamic link management, intelligent memory tiering, and energy-efficient controller operation. Machine learning classifiers built on historical telemetry data enable predictive maintenance capabilities that identify degrading links before service disruptions occur. Experimental validation across transformer training, large language model inference, and representative scientific computing kernels demonstrates substantial improvements in tail latency, aggregate throughput, and energy efficiency. The article provides practical guidance for fabric architects designing next-generation disaggregated computing infrastructures while identifying critical challenges and opportunities in scaling these approaches to hyper-scale deployments.
Architecting Applied AI–Driven Enterprise Analytics Platforms on Modern Data Warehouses
The rapid adoption of applied artificial intelligence (AI) in enterprise environments has intensified the need for scalable, governed, and high-performance analytics platforms. This study examines the architectural principles and design considerations for building applied AI–driven enterprise analytics platforms on modern data warehouses. Using a design-oriented and empirical analytical approach, the research evaluates how data warehouse–centric architectures support diverse AI workloads across performance efficiency, scalability, governance, and model lifecycle management dimensions. The results demonstrate that architectures leveraging elastic compute, integrated metadata services, and automated model orchestration achieve superior analytical performance, stronger compliance readiness, and improved operational stability compared to fragmented or legacy-extended platforms. Visual synthesis through multi-dimensional and cluster-based analysis further reveals that AI-native warehouse architectures align more effectively with enterprise trust and latency requirements. The study concludes that modern data warehouses serve not only as data repositories but as strategic analytical substrates for operationalizing applied AI at enterprise scale.
Continuous Evaluation & Observability for Enterprise AI Agents: A Unified Framework for LLM and ML Systems
The emergence of AI agents combining large language models with traditional ML components has created evaluation challenges that existing monitoring approaches cannot adequately address. This article presents a unified continuous evaluation framework designed for hybrid AI agent systems in enterprise environments. The framework integrates telemetry collection, drift detection, safety assessment, and business outcome measurement into a cohesive architecture. Through systematic analysis of framework components and implementation patterns, this work establishes theoretical foundations for reliable AI agent evaluation while addressing technical performance and business alignment requirements. The unified architecture incorporates reinforcement learning from human feedback, synthetic test generation, and advanced observability infrastructure to create a foundation for enterprise AI deployment. This framework addresses gaps in current evaluation methodologies by providing structured approaches to semantic assessment, multi-turn consistency validation, and business outcome correlation for AI agent systems.
Unified Data Foundations and Graph-Based Temporal Modeling for Healthcare Service Operations: Architectural Patterns Supporting Omnichannel Pharmacy and Payer Workflows
Pharmacy service centers and payer operations demand instantaneous access to prescription status, benefit determinations, authorization outcomes, and fulfillment tracking data. Organizational realities frequently involve disconnected platforms handling claims adjudication, eligibility verification, authorization management, dispensing logistics, and relationship tracking separately. Staff members reconcile contradictory snapshots while patients wait longer for therapy initiation. This article suggests a flexible plan that brings together different data systems with a focus on time-based modeling. Lifecycle signals come together using standard event-sourced structures, creating operational views that can be accessed through multiple channels at the same time. Saga coordination patterns merge with command-query separation strategies for multi-step process management. The system protects privacy by using access methods based on user attributes and roles, which work together with rules for handling data and secure logging. Transaction capacity meets enterprise demands while propagation speeds approach real-time thresholds. An accompanying measurement approach links architectural choices with service quality indicators. Observational data from deployment scenarios revealed shortened call durations for status inquiries and fewer repeated contacts. Such evaluation methods permit rigorous assessment of machine-learning-enhanced agent tools without sacrificing regulatory adherence.
Unified Payment Ecosystem Architecture (UPEA): A Comprehensive Framework for Modern Payment Processing
The multiplication of payment instruments, including customary banking rails, card networks, and digital-native wallet infrastructure, creates a level of complexity in today's payment ecosystem like nothing else. Today's payment processing stack is designed mainly to optimize for these single-instrument flows, but it does not easily scale, economically account, automate compliance, and optimize revenue. This paper describes an enterprise payment platform model (Unified Payment Ecosystem Architecture—UPEA) that combines payment processing, fund movement orchestration, risk & compliance, settlement, billing, and financial operations together. It addresses the systemic and calculated pain points in payments (heterogeneous financial instruments, multiple settlement timelines, manual reconciliation workflows, and revenue leakage) through five foundational principles: canonical data abstraction, event-driven fund movement, inline risk & compliance enforcement, ledger-backed financial integrity, and continuous routing optimization. Through implementation patterns, quantitative benchmarks, and mathematical models of transaction routing, treasury liquidity optimization, and revenue assurance, the case study shows how a unified payment architecture can considerably lower processing costs, improve near real-time visibility to settlement data, ease the burden of preparing for audits, and improve merchant satisfaction through consistent processing behavior and predictable settlement timing. These properties further establish payment infrastructure as a financial operating system that is regulatory resilient and platform sustainable.
Legacy Core Modernization via Strangler-Fig with Micro Frontends: Risk-Reduced Migration Patterns: A Case Study with Defect/Incident Deltas
Legacy core systems present significant challenges to enterprise organizations through operational inefficiencies, limited scalability, and constraints on digital innovation. Traditional big-bang replacement strategies carry substantial risks including extended timelines, high failure rates, and business disruption. Incremental modernization patterns offer viable alternatives by enabling gradual transformation while maintaining operational continuity. The strangler-fig pattern facilitates backend evolution through progressive routing of requests from legacy components to modern services. Micro frontend architectures complement this by decomposing monolithic user interfaces into independently deployable modules aligned with business domains. Anti-corruption layers prevent architectural degradation by translating between legacy and modern system semantics. Together, these patterns enable controlled technical debt management, fault isolation, and continuous value delivery. Organizations adopting these incremental strategies experience reduced deployment risks, improved system performance, enhanced team autonomy, and better incident containment. The architectural decomposition supports parallel development streams, accelerates delivery cycles, and maintains production stability throughout transformation initiatives. This evolutionary modernization framework provides enterprises with practical pathways to replace decades-old systems while preserving business continuity and institutional knowledge.
Stress Engineering Challenges in Mega Liquefied Natural Gas Projects: A Comprehensive Framework for Cryogenic System Design and Risk Mitigation
The mega onshore liquefied natural gas plants are larger than previous liquefied natural gas plants, and they involve different stresses. Therefore, a article is needed in order to provide risk analysis for satisfactory and safe operation. These high level of loadings and interactions between the imposed thermal contraction due to the cryogenic conditions lead to very high stress concentrations. The nonlinear material behavior in cryogenic conditions is very important for the distribution of these stresses through large piping systems. The design of cryogenic piping systems, which must deal with differential contraction, accepts piping flexibility. For the protection of equipment, critical nozzle loads, i.e. loads at pipe nozzles arising from equipment, must be defined. A support philosophy must therefore accommodate both the restraint and thermal effects. 3-dimensional stress analysis can be used to resolve the various loadings and complex geometries which require consideration in a mega scale plant. Risk assessment methodologies, design factor selection and reliability centered maintenance programs contribute to lowering the risk of anchor point failure leading to catastrophic stress failure, nozzle overload, and supporting structural failure. The interface management between modularized construction and stick-built construction methodologies, and the understanding of how construction sequences impact operational load profiles in mega LNG facilities contribute to the safety margins and operational reliability.
Temporal Consistency Models for Financial Data Processing in Distributed Systems
For most distributed financial systems, the constraints imposed by the CAP (Consistency, Availability, Partition Tolerance) theorem must be reconciled against the ordering constraints needed to satisfy regulatory requirements and meet the performance requirements of real-time transaction processing. This paper presents the Temporal Sequence Barrier consistency model for asynchronous high-throughput ledger systems. Combining logical vector clocks with epoch-based orchestration patterns imposes a strict causal ordering of events across multiple geographic regions without sacrificing availability. Its database-centric architecture allows stateful routing and selective replication of entities in order to achieve linearizability of causally related transactions while allowing independent sets of entities to be processed in parallel. We provide a detailed evaluation that shows that we can provide causal consistency at latency bounds equal to or better than existing systems using clever buffering and adaptive timeouts, while also addressing the classic challenges in distributed transaction management and operator complexity.
Cognitive Load as a First-Class Constraint in Enterprise Platform Engineering
Traditional engineering of enterprise platforms has focused on minimizing costs, achieving performance thresholds, meeting availability goals and security compliance without taking into account a basic human constraint: cognitive load of developers. An overload of mental work directly negatively affects the productivity of engineering, prolongs the practice of onboarding, increases the level of operational errors, and contributes to professional burnout. This document makes cognitive load one of the architectural constraints of the first order, as important as the traditional technical considerations. There has been evidence that cognitive burden beyond optimal levels leads to quantifiable degeneration in the various facets of an organization. These choices of platform design, such as tool sprawl, disjointed workflows, inconsistent abstractions, and insufficient documentation, are systematically associated with extraneous cognitive load, depleting the limited working memory capacity. The suggested framework combines the cognitive load theory and practices of practical platform engineering, providing methods of measurements that include both operational proxies and fragmentation indicators and formal developer feedback. Application of cognitive load awareness in distributed organizations brought about major gains such as reduction of defects, employee experience and customer satisfaction. The primary constraint of treating a cognitive load allows organizations to make delivery cycles faster without reducing reliability, knowledge retention, and team stability and, eventually, provide sustainable speed and improved organizational performance.
From Retrieval to Cognitive Orchestration: Standardizing Context Management in Agentic AI Systems
The proliferation of large language model-based agentic systems necessitates rigorous systems engineering approaches to context management. Contemporary frameworks, including Retrieval-Augmented Generation (RAG), ReAct, AutoGPT, and LangGraph, demonstrate autonomous capabilities but lack formal system specifications for context lifecycle, provenance tracking, and governance enforcement. This paper presents a systems engineering framework formalizing cognitive orchestration as a layered architecture with explicit invariants, interface contracts, and verification protocols. We introduce formal system models defining context as C = (K, M, P, T, V) with mathematical invariants ensuring consistency, completeness, and auditability. Our framework integrates Model Context Protocol (MCP) interfaces, establishing standardized contracts for agent coordination, memory management, and policy enforcement. Comparative analysis reveals systematic limitations in existing frameworks: RAG lacks multi-step context propagation (hallucination amplification 3.2×), ReAct exhibits unbounded memory growth (O(n²) with interaction length), AutoGPT suffers governance gaps (31% compliance violations), and LangGraph provides insufficient provenance tracking (34% audit coverage). Empirical validation through enterprise deployment, Annual Report Financial Analysis system processing 500+ documents across 15 regulatory frameworks, demonstrates quantifiable improvements: 94% reduction in compliance violations, 89% decrease in error propagation, 98% provenance completeness, and 3.1× mean time between failures compared to baseline architectures. System verification confirms invariant preservation across 10,000+ agent interactions with zero safety violations. This work establishes cognitive orchestration as essential infrastructure for production-grade agentic systems, providing formal foundations, architectural blueprints, and verification methodologies applicable across enterprise automation, financial analysis, regulatory compliance, and safety-critical domains.
Governance-Aware AI Microservices for Adaptive Enterprise Automation in Regulated Cloud-Native Systems
Regulated enterprise automation platforms face a fundamental tension between adaptive operations and requirements for explainability, auditability, and strict policy compliance. Traditional governance models using external policy engines, manual audits, and post-hoc reporting cannot scale to distributed microservices architectures where decisions occur continuously across multiple service boundaries. The governance-aware AI microservices framework embeds compliance controls directly into cloud-native system architecture through domain-aligned services, explicit policy enforcement points, bounded learning mechanisms, and comprehensive event-driven audit trails. By integrating AI augmentation within controlled boundaries and implementing role-aware decision routing, organizations achieve automation scalability while maintaining regulatory accountability and operational transparency. The framework addresses critical challenges, including distributed decision-making across multicloud environments, identity sprawl in cloud-native systems, and substantial financial costs of governance failures. Through architectural patterns treating governance as an intrinsic system capability rather than an external concern, organizations transform automation from a potential compliance liability into a resilient, accountable enterprise capability that aligns technical capabilities with organizational governance requirements.
Banking-as-a-Service (BaaS): Rethinking the Bank as an API
Banking-as-a-Service has emerged as a transformative architectural paradigm that fundamentally restructures how financial services are conceived, developed, and delivered across modern economic ecosystems. This comprehensive article explores the sophisticated three-layer technical architecture that enables non-financial organizations to embed complex banking functionality into their applications while maintaining regulatory compliance and operational security. The article investigates how licensed financial institutions, BaaS middleware providers, and client applications collaborate through API-driven integration patterns to create seamless embedded finance experiences that extend far beyond traditional banking boundaries. Through a detailed analysis of implementation patterns across neobanks, enterprise applications, and cross-industry embedded finance solutions, this article reveals how BaaS platforms successfully abstract the complexity of legacy banking infrastructure into developer-friendly interfaces without compromising the regulatory oversight and consumer protections essential for financial system stability. The article encompasses critical operational considerations, including distributed risk management, multi-jurisdictional compliance coordination, and advanced security frameworks that enable secure data transmission across organizational boundaries. Case studies demonstrate how successful BaaS implementations have revolutionized customer acquisition strategies, operational efficiency models, and revenue generation approaches across diverse market segments. The article also examines emerging technological developments, including the integration of artificial intelligence, compatibility with central bank digital currency, and international regulatory harmonization efforts that will shape the future evolution of banking infrastructure. These articles contribute valuable insights into how Banking-as-a-Service enables unprecedented innovation velocity while preserving the foundational stability requirements that underpin consumer trust and systemic financial security, positioning this architectural approach as essential infrastructure for the continued evolution of digital.
Digital Twin Enablement through Automated PLM Deployments: Accelerating Hardware Product Development
The use of digital twin technology radically revolutionizes hardware product development through the real-time synchronization of physical and virtual equivalents. Seamless integration of Product Lifecycle Management platforms with sophisticated engineering and manufacturing workflows is necessary to achieve the full potential of digital twins. This material describes an automation-centric approach to facilitating digital twins through optimized PLM deployments that utilize infrastructure automation and config management to accelerate environment setup, minimize integration complexity, and maintain consistency between development, QA and Production. The article utilizes infrastructure-as-code processes, containerization platforms, and declarative config management to normalize deployment processes while removing the need for manual configuration bottlenecks. Deployment on a big-ticket aerospace hardware program shows significantly faster environment setup times, accelerated iteration cycles for product verification, and improved integration between engineering and operations staff. The findings indicate that auto-deployed PLM is a key enabler of digital twin adoption, enabling more agile and cost-effective development of hardware products. Companies that deploy the framework realize stunning reductions in configuration drift events, expedited design verification cycles, and enhanced simulation correlation to physical test results, making digital twin technology a strategic differentiator in competitive production settings.
A Framework for Social Determinants of Health Integration in Population Health Management Systems
Traditional models of health care delivery mainly involve health care approaches while overlooking non-health issues that influence health care outcomes and health care expenditures. Social determinants of health relate to poverty, educational attainment, housing status, accessibility of transport, and food availability. Addressing such determinants requires systematic capabilities within population health management platforms. A dual-layer risk assessment framework provides comprehensive identification of social needs across populations. Community-level scoring leverages census tract geography and publicly available socioeconomic data. Six primary domains, including education, employment, income, transportation, housing, and food access, contribute to cumulative geographic risk calculations. Member-level assessments capture individual circumstances through structured screening instruments administered during care management encounters. Individual scores override community-level assignments when documented personal needs diverge from neighborhood averages. Risk stratification algorithms classify populations into tiered categories based on cumulative scores. High-risk individuals receive intensive care management with dedicated program enrollment. Medium-risk individuals receive educational materials and periodic monitoring. Configurable parameters enable organizational customization of scoring weights and threshold boundaries. Integration with community-based organization referral platforms enables closed-loop referral management. Electronic referral transmission and status tracking support regulatory compliance requirements. It translates disparate efforts within social care into structured and trackable interventions within healthcare populations.
AI-Assisted Incident Response: Engineering Safety into Automated Operations
Modern distributed systems present unprecedented challenges for incident response, with telemetry volumes and architectural complexity overwhelming human cognitive capacity during critical outages. This article examines the integration of large language models as copilots for incident management, proposing a comprehensive framework that balances the speed advantages of artificial intelligence with rigorous safety controls. The article identifies three critical failure points in incident response—sense-making across disparate telemetry sources, hypothesis generation under stress, and safe mitigation execution—where AI assistance shows promise but also introduces significant risks, including hallucination, privilege boundary violations, and lack of production constraint awareness. Drawing on frameworks for AI risk management, software supply chain security, and human-AI collaboration, the article presents a three-phase architecture separating sensing, deciding, and acting with mandatory human validation gates between transitions. The proposed multi-layer safety framework encompasses data governance through automated redaction and schema validation, privilege architecture implementing separation of duties and risk budgets, verification mechanisms including counterfactual checking and shadow execution, and comprehensive auditability through immutable decision ledgers. Human-AI collaboration patterns emphasize augmentation rather than replacement of human judgment, with AI providing rapid data synthesis and pattern matching while humans contribute contextual reasoning, ethical judgment, and final decision authority. The framework demonstrates that bounded automation with explicit oversight can reduce detection and restoration times while preserving the reliability guarantees and accountability requirements that production systems demand, offering organizations a practical path to leveraging AI assistance without compromising operational safety.
Optimizing Customer Lifetime Value with Reinforcement Learning
Sequential customer management decision-making needs dynamic strategies. Reinforcement learning provides computational models that train algorithms to respond optimally. Q-learning algorithms hold estimates of state-action values, which allow systems to determine the engagement strategies that yield the highest long-term payoff. Deep reinforcement learning enhances these abilities that can recognize more intricate behavior patterns. Optimization of interaction frequency helps balance customer engagement and fatigue, which reduces responsiveness. Personalization technology selects promotional materials based on individual tastes and buying records. The allocation of incentive plans provides discounts and rewards to segments that demonstrate the highest profit potential. Experimental validations compare reinforcement learning against rule-based heuristics and supervised prediction models, resulting in significant advances in retention rates and transaction values. The implementation problems include reward function specifications to align with business goals, exploration strategies to identify new approaches, and deployment architectures for real-time decisions for a large customer base.
A Zero‑Trust Compliance Architecture for LLM‑Integrated Pharmaceutical IT Systems: Securing AI‑Assisted Workflows with Data Integrity and Regulatory Controls
LLMs are being used in regulated pharmaceutical IT workflows for standard operating procedure (SOP) generation, deviation triage and Corrective and Preventive Action (CAPA) writing, but this is complicated by stochastic responses and susceptibility to prompt injection along with issues of data integrity, role-based access control and auditability. This paper proposes the Zero‑Trust Compliance Architecture (ZT-LLM-COMPASS) for this zero-trust setting, where every model invocation, context retrieval, tool invocation, and artifact generation is treated as a potential attacker payload. ZT-LLM-COMPASS incorporates four LLM Security Planes: fine-grained authorization via Identity & Policy Enforcement Plane, prompt firewalls and controlled retrieval via Prompt & Context Security Plane, least-privileged permissions on function calls via Tool Sandbox, and tamper-obvious audit packages via Evidence Plane. Human-in-the-loop verification gates ensure that the LLM can only propose and never commit regulated records. Adversarial evaluation demonstrates that the design is strong against injection attacks, leakage, and auditing challenges, enabling high AI productivity while retaining regulatory compliance.
Democratizing High-Performance Computing: How Virtualization and Workload Mobility Enable AI/ML Accessibility Across Organizations
The rapid increase in demand for GPU-accelerated compute for AI and machine learning workloads has outpaced the ability of many organizations to acquire, instrument, and manage dedicated pools of high-performance GPUs. Innovations in GPU virtualization and workload mobility have provided an alternate model of pooling, sharing, and migrating GPUs across heterogeneous infrastructure with strong performance isolation and quality of service characteristics with guaranteed performance bounds. The article proposes architectural and operational techniques to adapt virtualization-based GPU sharing and workload migration to enterprise data centers, edge and constrained installations, and air-gapped environments. Evaluation of production deployments reveals that, compared to legacy state-of-the-art systems, virtualization-based pooling allows sustained GPU utilization at higher rates while achieving almost native performance on compute-intensive workloads. Beyond their operational efficiencies, workload mobility and TCO reduction allow academic institutions, startups, and resource-constrained organizations to participate in AI workloads. The results show that virtualization and workload mobility are critical to democratizing access to accelerated computing and, at the same time, meeting the security, reliability, and performance needs of enterprise data science workflows.
Building Real-Time Pricing Systems for Modern Retail
Real time pricing systems have become core operational systems in modern retail, enabling organizations to respond dynamically to market conditions while maintaining consistent prices across diverse customer touchpoints. This article examines and systematizes key architectural foundations, decision making frameworks, and governance mechanisms required to build scalable real time pricing engines in cloud native environments. The architectural discussion emphasizes layered system designs that separate concerns across data ingestion, processing, storage, and delivery, with particular attention to trade-offs between latency, consistency, and correctness in distributed retail pricing systems. A key contribution is the analysis of hybrid decision making frameworks that integrate rule based constraint layers with machine learning models for demand elasticity estimation. The rule based layer runs deterministic guardrails that include minimum margin requirements, regulatory compliance constraints, and brand positioning policies. The predictive models estimate price sensitivity across heterogeneous product portfolios and customer segments. This integration requires explicit orchestration mechanisms such as validation pipelines and approval workflows to balance operational efficiency against governance oversight. The real time processing architectures revisit event driven paradigms, where automation of evaluation workflows is triggered by incoming market signals such as competitor price changes or inventory depletions. This addresses critical challenges in latency optimization, cross channel price synchronization, and consistency management across geographically distributed infrastructure. The article examines frameworks for measuring financial outcomes and operational metrics and evaluates testing approaches used to validate pricing behavior under failure scenarios. Ethical considerations are addressed through an analysis of fairness and transparency in algorithmic pricing decisions, with an emphasis on mechanisms that foster consumer trust. The final aspect is risk control measures with audit mechanisms. This comprehensive treatment bridges data engineering principles, economic theory, and operational governance, providing practitioners with conceptual frameworks for designing pricing systems that balance competitive responsiveness with business integrity, customer trust, and regulatory compliance in an increasingly dynamic retail landscape. This article makes three contributions: (1) a reference architecture for cloud native real time pricing systems, (2) a hybrid decision framework integrating rule based constraints with machine learning models, and (3) a governance and risk management model addressing operational, ethical, and regulatory concerns.
Digital Twin Architecture for Therapy and Imaging
Digital twins—computational representations of devices, environments, and patient physiology—offer transformative potential for medical device development by enabling simulation-based evaluation of scenarios that are difficult, dangerous, or impossible to reproduce physically. However, deployment in regulated environments demands rigorous credibility frameworks addressing verification, validation, and uncertainty quantification. This article proposes a comprehensive digital twin architecture tailored to regulated medical devices spanning therapy delivery and imaging workflows, emphasizing credibility as the foundation for trustworthy simulation evidence. The architecture decomposes platforms into core components, including device models capturing software logic and actuation behaviors, physics and environment models representing electromagnetic coupling and imaging physics, physiology models encompassing anatomy and tissue response dynamics, and data assimilation modules enabling patient-specific parameter tuning. Central infrastructure elements, including twin orchestrators, registries, and evidence layers, provide coordination, version control, and comprehensive documentation supporting reproducibility and regulatory compliance. The credibility framework establishes systematic protocols for code verification, solver verification, numerical stability assessment, and stratified validation across device settings, patient anatomies, and workflow variants, with explicit uncertainty quantification and traceability from risk controls to simulation evidence aligned with regulatory standards. Risk-informed applications enable hazard-to-scenario mapping, boundary condition exploration, fault injection testing, and combined fault mode analysis that expand verification coverage beyond traditional testing limitations. Controlled personalization frameworks define permissible parameter spaces and safety envelopes, treat patient-specific parameter estimation as verified algorithms rather than unbounded learning processes, implement privacy preservation through data minimization and secure governance, and establish guardrails preventing unsafe adaptations. Deployment considerations address computational infrastructure requirements, reproducibility protocols through containerization and numerical tolerance management, tool qualification processes for commercial and custom simulation codes and lifecycle management, ensuring version control, change management, and evidence archival throughout product lifecycles. The article concludes by examining broader development implications, including prototype reduction and shorter development cycles, alongside future directions encompassing standardized credibility reporting, shared physiological model libraries, and hybrid physics-data approaches integrating machine learning within physics-based constraints to maintain explainability and bounded behavior essential for regulatory acceptance.
The "Performance First" Paradigm: Practical Tips for Building High-Speed Mobile Applications
Mobile application performance has become the defining factor in user retention and satisfaction, yet most development teams continue addressing speed issues reactively after deployment rather than designing for performance from the outset. This article introduces the Performance First Paradigm, a comprehensive framework that positions application speed and responsiveness as foundational engineering requirements rather than optional enhancements. The article integrates three interdependent dimensions: technical optimization strategies, architectural discipline, and organizational collaboration. Technical approaches include progressive dashboard loading, intelligent caching mechanisms, asynchronous network orchestration, and API bridging layers that minimize redundant service calls and streamline data flow between legacy and modern systems. Architecturally, the framework advocates restructuring application workflows to prioritize essential data immediately after authentication while deferring non-critical content. Organizationally, it emphasizes establishing cross-functional performance councils that bring together developers, designers, and backend engineers to align around shared speed targets and continuous monitoring practices. Drawing from real-world implementation experiences across diverse mobile environments, the paper demonstrates how teams can achieve substantial latency reductions and improved scalability by embedding performance considerations throughout the product lifecycle. The insights provide mobile engineering leaders with actionable strategies for building applications that meet contemporary user expectations for instant responsiveness in increasingly competitive digital markets.
AI Identity for Agentic Systems: Integrating PAM and IAM for Secure Autonomy
Agentic AI systems, as autonomous agents that make decisions and take actions in enterprise environments, raise identity governance questions regarding agents that maintain state, collaborate, and request escalation of privileges to perform complex activities, different from the questions that human actors, service accounts, and API tokens raise in identity governance contexts. The article outlines the existing challenges of establishing customary IAM for agentic AI and proposes a thorough framework building on IAM and PAM elements for holistic oversight of agentic AI. This framework entails considering agentic AI as first-class identity subjects, being responsible for their lifecycle, and dynamically provisioning their credentials. It also provides context-aware policy enforcement and continuous behavior monitoring. Augmenting IAM's identity controls with PAM's privileged operations governance satisfies operational autonomy and security requirements, increases accountability and compliance, and ensures the trustworthiness of autonomous AI operations while countering privileged credential theft, privilege escalation, and audit trail deficiencies stemming from customary identity-centric approaches to PCC management of autonomous AI operations.
Sustainable Rehabilitation of Oil-Contaminated Sand: Enhancing Mechanical Properties through Alluvial Sand Substitution and Polypropylene Fiber Reinforcement
The waste generated by drilling activities in the hydrocarbon industry poses a significant environmental threat, making its management essential. One effective approach is to repurpose this waste for sand concrete production, providing a sustainable alternative to conventional sand concrete. This study experimentally investigates the feasibility of incorporating oil well sand into sand concrete reinforced with polypropylene fibers. The oil content in the well sand was reduced from 20% to 5% by partially replacing it with alluvial sand in varying proportions (20% and 30%). Fiber reinforcement was introduced at dosages of 1 and 1.5 kg/m³. Reducing the oil content by incorporating alluvial sand improved both the fresh and hardened properties of the sand concrete. The optimal mechanical performance, in terms of compressive and tensile strength, was achieved with a mixture containing 30% alluvial sand and 70% oil well sand. A fiber dosage of 1.5 kg/m³ had a more pronounced effect on the compressive and tensile strength, as well as the shrinkage behavior, of the mixtures containing 30% alluvial sand and 70% oil well sand.
AI-Driven Network Security Architecture for Edge Computing
Edges pose a fundamental change to network security architecture. Edge computing moves computation, and thus exposure, to many physical locations, making customary enforcement points ineffective. Security architecture must consider the dynamic topology and variable connectivity, as well as the huge attack surface of billions of devices generating large volumes of data on the edge of the network. Distributed intelligence architectures implement a tiered security processing model. Security processing occurs on both edge and core compute, enabling real-time threat detection with low-overhead machine learning inference on the edge and centralized correlation analysis. APIs support programmable, policy-based automation of security across multi-vendor infrastructure, including zero-touch device provisioning and intent-based security operations. More advanced threat detection relies on behavioral baselining, predictive modeling, and ensemble learning to detect threats across multiple threat classes with a high level of precision and recall. Zero-trust architectures replace binary authentication with continual risk assessment and dynamic policy application. Zero-trust networks enforce micro-segmentation to restrict lateral movement and reduce an attack's blast radius. To extend advanced security capabilities to resource-constrained edge devices, model compression and energy-efficient inference at the edge are popular approaches to hardware acceleration. It can also considerably reduce the computational cost while maintaining detection performance with this integrated framework to address the security problems in distributed computing environments through smart automation, adaptive controls, and resource optimization.
Research and Innovations in Identity and Access Management: Emerging Paradigms for Financial Services
Identity and Access Management has undergone a foundational architecture shift, based on the convergence of artificial intelligence, Zero Trust, and decentralized identity. Perimeter-based identity governance approaches, with static role provisioning and periodic access review, are structurally inadequate for the identity surfaces that modern financial enterprises operate today. Human users, service accounts, APIs, containerized workloads, and autonomous agents require context-sensitive, always-on access controls that run at a speed and granularity that existing access control and identity management technologies cannot accommodate. Seven innovation domains are changing the landscape of access control and identity management. Agentic artificial intelligence shifts access governance from reaction to prediction. Continuous behavioral authentication reduces implicit session trust. Just-in-time privilege reduces time of access to only what is needed to perform a task, while blockchain-anchored decentralized identity enables privacy-preserving credential verification. Machine identity governance extends least-privilege access to non-human identities. Platform convergence unifies fragmented access governance. Compliance frameworks require machine-speed policy enforcement. The financial services sector, with its heavily regulated environment, high-value targets, and desire for frictionless access experiences, serves as an unmatched proving ground for the approaches presented here. Some of the key open research questions are explainability for regulated decision trails, quantum-resistant crypto, privacy-preserving federated learning, and the measurement of return on investment.
Automated Root Cause Analysis in Distributed Cloud Environments: An Unsupervised AIOps Approach Using BigQuery ML
Modern distributed cloud environments present unprecedented challenges for operational monitoring and incident management, where traditional rule-based systems fail to effectively handle the scale and complexity of containerized microservices architectures. This article presents an advanced AIOps framework that leverages unsupervised machine learning techniques through BigQuery ML to automate anomaly detection and root cause analysis in distributed cloud systems. The framework aggregates heterogeneous telemetry data from container orchestration platforms, application traces, and network flow logs into unified analytics pipelines that establish dynamic operational baselines without requiring labeled training datasets. The implementation employs clustering algorithms and statistical deviation detection mechanisms to identify anomalous patterns across multiple system layers while suppressing operational noise and false positive alerts. Experimental evaluation in production environments demonstrates substantial improvements in detection accuracy, significant reductions in mean time to detection and resolution, and enhanced operational efficiency for site reliability engineering teams. The SQL-based machine learning implementation enables operations teams to deploy sophisticated analytics without specialized data science expertise, providing scalable anomaly detection capabilities that adapt to evolving operational patterns in dynamic cloud environments.
Benchmarking Predictive Autoscaling vs. Horizontal Scaling for Large-Scale Data Pipelines: A Real-Time FinOps Evaluation
Autoscaling is an essential facilitator of stability of performance and cost at cloud-native data platforms, but most empirical analyses are conducted with stateless web services and microservice-based architectures as opposed to data engineering workloads. A common set of data pipelines, such as ETL flows, streaming analytics, batch processing, and Spark-based distributed computations, uses the characteristics of stateful execution, bursty demand patterns, DAG-based scheduling, and extreme sensitivity to scaling latency. Machine intelligence-based predictive autoscaling methods like ARIMA, Prophet, LSTM, and workload archetype classifiers have the potential to provide proactive resource provisioning in the future by anticipating a demand value, thus eliminating SLA breaches and tail latency. But these predictive strategies are not done with strict benchmarking relative to the conventional reactive horizontal autoscaling in the context of data-centric workloads running in the domain of real-world variability. At the same time, FinOps structures promote cloud cost visibility and efficiency, but there is no standardized metric that can measure the cost-performance trade-offs of predictive and reactive scaling schemes in a variety of pipeline structures. This benchmark presents a full-system assessment model that integrates workload traces recorded in the past with synthetic replay workloads replicating enterprise data pipeline behavior, including bursty ingestion, periodic batch cycles, and multi-stage DAG executions. The predictive, reactive, and hybrid autoscaling options are tested on Kubernetes and Spark clusters with an extensive metric suite, which combines engineering performance indicators with cost-related dimensions and FinOps-oriented metrics. The resulting construct gives objective foundations of autoscaling decision-making and provides real-world guidelines and artifacts of evaluation to organizations that want to streamline cloud spending without sacrificing pipeline stability or operational robustness.
Autonomous AI Agents for Apache Flink Pipeline Management on Kubernetes
Modern companies struggle to maintain data pipelines that process millions of events per second while meeting strict performance requirements. Traditional methods use fixed configurations and manual fixes, which fail when workloads change unexpectedly. This paper presents an AI-powered system that automatically manages Apache Flink pipelines on Kubernetes. The system uses machine learning to predict problems before they occur, recover from failures automatically, and optimize resource usage continuously. The system was evaluated using two publicly available benchmark datasets: the NYC Taxi Trip Record dataset adapted for streaming scenarios and the Yahoo Cloud Serving Benchmark dataset. Tests show the AI-driven approach significantly reduces service violations, substantially cuts recovery time, and lowers infrastructure costs compared to manual management while maintaining better performance. The system uses three AI agents working together where the prediction agent forecasts problems ahead of time with high accuracy using a neural network that processes multiple metrics continuously, the recovery agent detects failures rapidly using isolation forests, autoencoders, and long short-term memory networks, and the optimization agent adjusts resources dynamically based on workload patterns using reinforcement learning. Together, these agents enable the system to operate autonomously, dramatically reducing manual interventions and operational overhead.
Prioritizing Safety Recalls Using AI-Driven Risk Models on Connected Vehicle Operating Data
Connected vehicle technologies enable unprecedented opportunities for differentiating safety recall urgency through AI-driven risk assessment frameworks that leverage real-time operational telemetry and historical maintenance records. Traditional recall campaigns treat all affected vehicles uniformly despite substantial variations in actual failure probability based on usage patterns, operating conditions, and component stress levels. The proposed framework combines gradient boosted tree models with temporal neural networks to generate calibrated risk scores that identify vehicles most likely to experience imminent safety-critical failures. Telemetry streams, including engine load distributions, thermal exposure patterns, braking system stress indicators, and diagnostic trouble codes, provide input features that capture both chronic degradation and acute anomaly signals. Risk-based prioritization enables service centers to schedule the highest-risk vehicles for expedited repairs while maintaining standard timelines for lower-risk units, optimizing allocation of constrained dealer resources, including service capacity and replacement parts inventory. Operational deployment through cloud-native streaming architectures processes high-velocity telemetry at scale with real-time scoring capabilities integrated into dealer management systems and customer notification channels. Explainability mechanisms using feature attribution methods provide a transparent rationale for individual risk classifications, supporting regulatory compliance and customer communication requirements. Empirical validation demonstrates substantial reductions in post-notification field failures when the highest-risk vehicles receive prioritized outreach compared to uniform notification strategies. Customer satisfaction improvements emerge from proactive communication that demonstrates manufacturer concern through personalized risk assessment and convenient scheduling options. Legal risk mitigation benefits arise from documented data-driven prioritization that strengthens the defensibility of recall processes in regulatory reviews and litigation contexts. The framework aligns automotive manufacturers to transform connected vehicle data into operational safety intelligence that enhances the outcomes on public safety, customer experience, operational efficiency, and legal exposure facets.
Real-Time Messaging with Hybrid-State Architectures: Optimizing Latency and Cost in Generative AI-Enhanced Real-Time Messaging at the Edge
Real-time messaging applications are rapidly integrating generative artificial intelligence capabilities, yet the prevailing cloud-only inference paradigm presents significant challenges in terms of cost, latency, and user privacy. As messaging platforms scale to serve millions of concurrent users, routing every user utterance through large cloud-hosted language models creates a Thundering Herd effect on centralized GPU clusters, inflating token costs and degrading time-to-first-token performance. Simultaneously, transmitting all raw user text to remote servers amplifies privacy exposure, particularly for sensitive data categories such as personally identifiable information and criminal justice information. This article proposes a hybrid-state architecture built upon a novel distributed inference protocol termed "speculative edge decoding." In this architecture, lightweight Small Language Models deployed on client devices handle simple tasks and generate draft token sequences, while Large Language Models hosted in the cloud perform verification passes rather than full generative inference. The system has a complexity router that automatically sorts incoming prompts and sends them to the right inference tier. It also has a dynamic low-rank adapter loading mechanism for task-specific specialization on the edge and trusted execution environments for secure cloud-side inference. Experimental evaluation in simulated high-load environments demonstrates substantial reductions in cloud infrastructure costs alongside markedly faster end-to-end response times, without meaningful degradation in response quality as measured by standard automated metrics. The article affirms that the future of scalable AI-enhanced messaging lies not in exclusive cloud dependence but in the intelligent orchestration of cloud and edge resources.
Architecting Intelligent Tax Automation: Research Innovations in Machine Learning for Global Compliance
The global indirect tax compliance of large-scale digital commerce platforms has become a complex, high-stakes systems problem due to jurisdictional fragmentation, regular change of regulations, and the high pace of expansion of heterogeneous product catalogs. Rule-based tax engines, although auditable and deterministic, fail to scale in such a situation because their authoring processes are fragile, maintenance is expensive, and their semantic knowledge of product data is limited. This article provides a detailed design of an intelligent tax automation system that is based on machine learning-based item-to-tax prediction services, supported by confidence-aware orchestration, human-in-the-loop protection, and explainability features that are appropriate in regulated financial settings. The suggested framework uses transformer-based language models that are trained on large-scale and multilingual commerce data to predict tax classifications directly based on item titles, descriptions, and structured taxonomy cues. Instead of using fixed mappings, the system is trained on semantic associations between product representations and jurisdiction-specific tax treatments, allowing it to correctly process long-tail, ambiguous, and newly added items. Calibrated confidence scores are provided with predictions, and this indicates whether the transactions can be safely automated, sent to policy validation, or sent to expert scrutiny. This is the selective automation model that balances operational efficiency and regulatory risk, compliance integrity, and scale. The architecture is deployed in a controlled machine learning system and combines continuous monitoring, auditability, and retraining pipelines based on feedback. The experience of large-scale deployments has shown that they can substantially reduce the effort required for manual rule formulation and scrutiny, increase the accuracy of classification into thousands of categories, and have a quantifiable financial effect, without compromising transparency to auditors and other regulatory stakeholders. The article defines intelligent, ML-driven tax automation as a feasible and responsible alternative to the legacy rule-based systems in the global compliance areas.
Compliance-Native Architecture for Co-Branded Credit Card Platforms: A Multi-Party Financial Integration Framework
Enterprise financial systems supporting co-branded credit card programs must reconcile the fast pace of innovation with highly regulated PCI DSS, SOC 2, GDPR and FFIEC standards. Existing systems allow compliance testing to be a post-deployment pass/fail validator, introducing gaps while incurring the overhead of remediating violations discovered via compliance testing and audits. Architecture can be compliance-native, as when policy is a system primitive, enforced at runtime by policy engines, and recorded by event-sourced deterministic workflows and cryptographically verifiable logs. Attribute-based access control (ABAC) and declarative policy engines run millions of policy evaluations daily. Sub-millisecond latency is often required. Event sourcing saves all changes as unchangeable ordered sequences so that full audit reconstruction and regulatory explainability are possible. Zero-trust integration patterns establish cryptographic identity proofs and fine-grained trust boundaries across multi-party ecosystems. Large language models help compliance processes by automatically interpreting regulations and providing clear guidelines to make sure people oversee important decisions. Other features include cryptographically verifiable audit logs based on Merkle trees, which can be independently verified for regulatory purposes. The evidence shows that compliance requirements are enabling constraints and not obstacles in the way of velocity and adoption. These empirical deployments in global distributed systems have shown considerably reduced compliance overhead (while preserving the audit trail) and a faster verification cycle. Together with AI-assisted compliance layers and deterministic enforcement mechanisms, these technologies create the basis for scalable, trusted financial systems that turn post hoc requirements into architectural properties.
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.
SCAPS-1D Numerical Optimization of ZnO:Al/PCBM/CsSnI₃/MoO₃ Perovskite Solar Cells
A numerical optimization of lead-free ZnO:Al/PCBM/CsSnI₃/MoO₃ perovskite solar cells is performed using the SCAPS-1D simulator. The effects of absorber and transport layer thickness, doping concentration, defect density, series and shunt resistances, and operating temperature on device performance are systematically investigated. The results show that the CsSnI₃ absorber thickness and defect density dominate recombination losses and efficiency, while the PCBM layer exhibits strong defect tolerance. Optimized absorber thickness (1.2 μm) and acceptor doping (1019 cm-3) significantly enhance charge collection and fill factor. Series resistance strongly degrades performance, whereas high shunt resistance is essential for achieving high open-circuit voltage. Under optimized conditions, a maximum power conversion efficiency of 25.39% is achieved, representing a theoretical upper limit for CsSnI₃-based single-junction solar cells. The study provides clear guidelines for defect passivation and interface engineering in lead-free perovskite photovoltaics.
Resilient Connectivity Patterns for Electric Vehicle Fleets
Electric vehicle fleet operations face significant connectivity challenges across heterogeneous network environments, requiring innovative solutions for seamless communication maintenance. This article presents a comprehensive connectivity architecture comprising Multi-Network Adaptive Routing (MNAR), Context-Aware Data Prioritization (CADP), and Predictive Connectivity Health Modeling (PCHM) frameworks designed to address critical gaps in current fleet communication systems. The MNAR framework enables dynamic network discovery and intelligent switching across cellular, Wi-Fi, satellite, and mesh technologies through real-time performance optimization algorithms. CADP implements hierarchical data classification and priority assignment mechanisms that optimize bandwidth utilization based on operational context and mission criticality. PCHM employs machine learning algorithms for connectivity degradation prediction and proactive network management strategies. The integrated security architecture addresses multi-network vulnerabilities through end-to-end encryption, zero-trust authentication, and comprehensive intrusion detection systems. Performance evaluation demonstrates substantial improvements in connectivity uptime, latency reduction, bandwidth efficiency, and cost optimization compared to traditional single-network approaches. The proposed architecture exhibits superior scalability across diverse fleet deployments while maintaining consistent performance characteristics. Implementation frameworks include microservices-based deployment, container orchestration, and blockchain integration for secure fleet-to-fleet communication. Validation through simulation environments and real-world pilot implementations confirms system effectiveness across urban, rural, and challenging operational scenarios. Future development opportunities encompass 5G network slicing integration, quantum-resistant encryption protocols, and AI-driven autonomous optimization capabilities with cross-industry applicability to maritime and aerospace domains.
Converged SIEM Framework for Unified IT-OT Security Monitoring
Critical infrastructure security has traditionally maintained a rigid separation between Information Technology and Operational Technology systems, creating dangerous blind spots that sophisticated threat actors increasingly exploit. This article presents a unified Security Information and Event Management framework that integrates telemetry from hardened endpoints, network firewalls, and industrial IoT sensors into a single correlation engine designed specifically for cyber-physical systems. The proposed architecture addresses the fundamental challenges of converged IT-OT security monitoring through three core pillars: standardized data ingestion accommodating heterogeneous protocols, normalized event processing establishing consistent taxonomies across domains, and cross-domain correlation logic capable of identifying sophisticated multi-stage attacks. By leveraging machine learning approaches, including convolutional neural networks and behavioral analytics, the framework enables the detection of subtle anomalies and previously unknown attack patterns while maintaining low false positive rates that minimize operational disruptions. The research demonstrates how automated asset discovery addresses the persistent challenge of shadow IT and unmanaged operational devices in dynamic industrial environments, while security orchestration and automated response capabilities streamline incident management workflows. Cross-domain context enrichment proves particularly valuable during incident response, revealing how digital compromises affect physical operations and ensuring that containment actions align with operational safety requirements. This converged approach provides comprehensive real-time visibility across the entire cyber-physical ecosystem, enabling security teams to detect threats that span both digital and physical realms, significantly reducing response times while accounting for the unique characteristics of operational technology environments, including deterministic timing requirements, legacy equipment constraints, and safety-critical operational continuity demands.
Reliability-First Architecture for Large-Scale Batch Data Pipelines
Massive data processing systems that process data at billions of records at a time have inherent challenges in which correctness and reliability are more important than computational throughput. The reality of operations shows that instances of data quality take up a significant amount of organizational resources by taking up time in different detection stages and prolong the resolution process, which leads to cascading failures that disseminate through the downstream analytic systems. Distributed computing systems bring with them intrinsic failure modes such as the repetition of tasks via the retry mechanism, speculative execution of tasks, partial output values after multipart object storage write requests, and temporal inconsistency as part of historical reprocessing actions. The solution to these reliability issues involves architectural patterns to convert distributed uncertainty to deterministic recovery by explicit idempotency guarantees with coordination stores, transactional commit protocols where multi-object outputs are atomically published, date-scoped dependency graphs yielding temporal consistency in the process of backfills, and checkpoint-based recovery mechanisms that limit the reprocessing window. Configuration parameters that are standard to an industry, such as those provided by Apache Spark and Apache Airflow, directly determine the operational risk profile, such as settings related to the number of attempts to make on retries, speculation levels, concurrency levels, and state retention policies. The patterns of architecture shown in it set up the principles of reliability-first design, in which failures are non-destructive incidents, as opposed to disastrous. Strategies of implementation exploit coordination structures with transaction boundaries, orchestration structures with definite concurrency envelopes, and streaming structures with retention checkpoints to establish pipelines, which fail gracefully and guarantee correctness. These methods move the engineering capacity of engineering response to the engineering productive feature development by making reprocessing operations yield deterministic results consistent with the original execution semantics.
The Automotive Data-Equity Loop: Converting Telemetry into Consumer-Owned Resale Value Through Verifiable Digital Passports
The transformation of vehicles into software-defined platforms creates a fundamental trust gap between consumers and entities seeking detailed telemetry data. Traditional incentive models, including direct payments and feature personalization, fail to motivate sustained telemetric sharing because they cannot overcome consumer concerns about surveillance, fairness, and asymmetric value extraction. This article introduces the Automotive Data-Equity Loop, a socio-technical framework that reframes telemetry sharing from data extraction to asset protection by converting operational data into durable, consumer-owned verified credentials that enhance resale value. The framework operates through three interconnected phases: value creation during ordinary vehicle use, value crystallization through privacy-preserving conversion of raw telemetry into tamper-resistant cryptographic credentials, and value realization when verified history reduces buyer uncertainty in resale transactions. The hybrid asset conceptual model positions vehicles as composite assets integrating physical components with verifiable digital history layers, leveraging behavioral-economic principles, including loss aversion and endowment effects, to align long-term consumer interests with platform data requirements. The transparency slider interface operationalizes graduated consent through multi-level controls mapping sharing choices to certification outcomes, while purpose-bound certificates restrict secondary use by design. Comparative analysis establishes testable hypotheses predicting that asset value enhancement outperforms alternative incentive structures through increased perceived fairness and reduced exploitation concerns. The governance blueprint addresses multi-stakeholder trust through explicit role definitions for credential issuers, verifiers, and marketplace participants, alongside threat models addressing consumer gaming, forgery attacks, and marketplace pressure dynamics. Implementation requires empirical validation through controlled experiments measuring adoption rates, persistence duration, and depth of consent, complemented by observational market studies assessing effects on resale prices, transaction velocity, and buyer confidence indicators. The framework demonstrates how consumer-generated data ecosystems can align with autonomy and long-term value creation when systems treat individuals as asset owners rather than extractable data sources, with implications extending beyond automotive contexts to broader Internet of Things and smart asset domains.
RPA Integration with Identity Management Platforms: A Scalable IAM Orchestration Pattern
The rapid proliferation of non-human identities across cloud and hybrid environments has exposed significant gaps in conventional identity governance architectures, which were designed primarily around human access patterns and periodic compliance assessment cycles. Integrating robotic process automation with identity governance platforms introduces a scalable orchestration model in which the governance platform serves exclusively as the policy and decision authority while automation agents execute provisioning tasks through a database-mediated communication layer. This separation of policy enforcement from execution responsibility eliminates uncontrolled privilege escalation and enables provisioning across applications that lack native connector support. Continuous behavioral authentication mechanisms, multi-interface communication redundancy, cryptographic audit trail enforcement, and event-driven remediation together form a unified control architecture that extends identity assurance beyond point-in-time verification into the full operational lifecycle. Quantifiable improvements in provisioning speed, compliance accuracy, breach risk reduction, and administrative cost confirm that governance-centric automation delivers measurable security and operational value across regulated enterprise environments.
Data Aggregation of HEDIS Measures using ETL Technologies
The Healthcare Effectiveness Data and Information Set (HEDIS) has standardized performance measures. These measures aim to confirm that the health care services provided and utilized are of high quality. For reporting HEDIS measures, the data must be collected and aggregated from a variety of sources, including electronic health records (EHRs), claims data, pharmacy data, and lab information systems. This article uses Extract, Transform, Load (ETL) technologies to orchestrate data integration processes and provide trustable measure calculations. In the extract process, heterogeneous data sources are connected to the ETL pipelines by database interfaces, application programming interfaces (APIs), and streaming ingestion protocols, with security and compliance requirements. This phase includes cleansing and validation checks, as well as standardizing and recoding all of the different coding systems and heterogeneous constructs into a common structure that is consistent with the HEDIS specifications. The loading phase involves complex calculations and performance reporting. Business benefits include improved data quality via data validation at the source, process efficiency via workflow automation and scalability, and better decision support with improved visibility for all stakeholders in real-time operational performance. Implementation considerations include enterprise platforms, open-source and cloud-native applications, interoperability via Fast Healthcare Interoperability Resources (FHIR) standards, and available software development kits (SDKs). As new technologies like real-time data processing, cloud computing, and AI-based validation develop, healthcare organizations will choose their reporting technology based on their size, technical skills, and available resources, while also improving data management and adapting to new standards and regulations in a data-heavy environment.
Method-Level Performance Instrumentation for Production Java Microservices
For production Java microservices, there is an observability gap in tracking infrastructure cost down to the method level of a source code change. Existing Java profilers either have low overhead sampling with short retention for unqueryable flame graph formats or rich events with high overhead that must be manually enabled during production. Neither approach supports automated regression detection workflows that fast-moving engineering teams need. A runtime instrumentation architecture supports (1) configuration-driven bytecode instrumentation, (2) adaptive per-method sampling that calibrates how much telemetry is collected from each application method based on how many times that method is called per second, (3) fail-open streaming telemetry that separates telemetry transport from application execution, and (4) SQL-driven automated regression detection over a columnar data warehouse with extended retention. It provides cost and latency telemetry at the method call level with low overhead, a structured queryable system, and long-term storage. It can automatically detect performance regressions, attribute costs per method, and provide systematic performance governance, without requiring source-level modification or manual profiling of the code under assessment.
Digital Worker in a Contact Center: A Technical Review
In general, the digital worker is a new type of technology used in contact centres today. Digital workers use artificial intelligence (AI) to automate repetitive and standard tasks that contact centre agents would typically perform, such as verifying an identity, managing customer accounts, and routing customer inquiries. The main benefit of digital workers is that they take on these standard tasks so that contact centre agents can spend more time resolving customer issues that require critical thinking or empathy. Digital workers support contact centre companies by providing a seamless interface with multiple systems, allowing for real-time access to the customer's account information and contextual understanding of the customer's needs. Partnering with digital workers enables companies to improve their operational efficiency and reduce customer wait times. By quickly and accurately completing standard transactions, digital agents increase customer satisfaction. As the technology continues to evolve through advances in natural language processing (NLP) and machine learning (ML), customer acceptance and integration challenges will continue to be critical to the successful deployment of digital workers in contact centres. The adoption of digital agents by the contact center represents the beginning of a new service delivery model, which blends human expertise with automated technology together in what can be referred to as a hybrid environment. This will ultimately allow the organisation's service delivery approach to evolve.
AI-Driven Integration Framework for Enterprise Logistics and Financial Systems: A Technical Review
Enterprise environments today wrestle with a fundamental problem. Legacy financial planning systems need to work alongside AI-driven automation platforms. This isn't just about connecting systems. Warehouse management, collaborative robotics, and enterprise resource planning create a web of technical dependencies. Traditional batch-oriented financial tools operate on different timescales than real-time operational systems. Manual processes and outdated middleware make seamless interoperability difficult. Organizations need integration layers that translate between different system architectures. Data consistency matters. Operational reliability cannot be compromised. Exception handling protocols must be robust. The validity of human oversight is a critical factor in processing and decision-making. The need for an audit trail to meet regulatory standards is also a major factor. Getting this integration right delivers substantial operational improvements. The framework addresses workforce adaptation concerns. Safety protocols protect workers and equipment. Cross-platform orchestration enables enterprise-wide visibility. Standardized exception handling can be replicated across facilities. Forecasting logic becomes reusable across domains. Scalability drives architectural choices from day one. Horizontal expansion and future integrations must be possible without major rework.