International Journal of Computational and Experimental Science and Engineering
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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.
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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.
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.
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.
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.
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.
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.
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.
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.