Financial institutions are adopting artificial intelligence (AI) to support decisions in areas such as risk assessment, customer recommendations, fraud detection, and operational automation. However, deploying AI in regulated environments is more difficult than deploying conventional software. AI systems can change in performance over time, depend heavily on data quality, and create added demands for explainability, traceability, and governance. As a result, financial institutions need deployment architectures that treat compliance, auditability, and resilience as built-in design requirements rather than after-the-fact controls. This article presents a compliance-aware AI deployment architecture for enterprise financial decision platforms. The proposed approach combines decision orchestration, policy-based control, model version management, approval workflows, monitoring, rollback, fallback mechanisms, and evidence capture. The architecture is designed to help organizations manage AI deployment in a controlled way while preserving accountability and operational continuity. The analysis shows that successful AI deployment in regulated financial environments depends not only on model accuracy but also on the surrounding production architecture. Explainability must be supported by decision-time metadata and traceable workflows, auditability must be enabled through versioned and reviewable deployment records, and resilience must be provided through monitoring and safe fallback paths. The resulting framework offers a practical foundation for scaling AI-enabled decision systems while maintaining regulatory compliance, operational reliability, and institutional trust.
Introduction: The development of intelligent support systems for minimally invasive surgery requires careful awareness of how safety is maintained in automated decision processes. Deep learning has notably contributed to the analysis of laparoscopic recordings, improving anatomical segmentation, phase detection, and gesture recognition. However, most known approaches remain limited to predictions. Although they may not fully describe how the operational situation advances over time or how actions should respond to that evolution. Objectives: This work aims to give a structured state representation that helps safe learning in surgical conditions, where reward and imitation learning cannot depend on unrestrained exploration. Methods: Laparoscopic surgery is modeled as a sequential and partially observable process. A multimodal state is generated by merging anatomical context, a safety indicator derived from the Critical View of Safety, gesture dynamics, procedural phase information, and active instrument configuration into a cohesive picture. Results: Experimental evaluation shows that the proposed structured state architecture produces a coherent representation of the dynamic surgical environment, allowing trustworthy learning from recorded expert procedures. Conclusions: This study reveals that such structured state building provides a cohesive platform for dependable data-driven help in complicated Healthcare 5.0 scenarios.
Modern enterprise platforms operate as distributed ecosystems spanning cloud-native services, edge computing infrastructure, and interconnected Internet of Things (IoT) environments. Traditional quality assurance models, designed for centralized architectures and sequential development lifecycles, are insufficient in environments characterized by asynchronous communication, heterogeneous execution layers, and physical-world dependencies. This article argues that quality engineering must be elevated to an architectural discipline capable of governing reliability across distributed systems. Through applied patterns drawn from large-scale enterprise deployments, it demonstrates how structured validation frameworks—embedded within system design and extended by observability-driven feedback loops—produce measurable improvements in synchronization integrity, transaction reliability, and cross-platform data consistency. Deployments adopting architecture-driven validation have achieved cross-platform data consistency rates exceeding 99% while reducing reconciliation discrepancies across distributed services. These outcomes support the position that quality engineering, when treated as foundational infrastructure rather than a downstream function, is the primary mechanism for sustaining reliability across cloud, edge, and IoT platforms.
Self-service search is typically a user's first interaction with support content. It translates an end-user's informal, noisy query text into a support answer. However, due to bursty search traffic, rapidly evolving content, and expensive retrieval features, it is challenging to maintain the online reliability of such web-scale search systems. The system is made up of several layers that can handle a lot of searches, including a main part that spreads out the search load and a backup index that helps manage problems and keeps response times steady. Routing is done via health checks and circuit breaker design patterns, allowing for graceful degradation in high load and partial failures. Bounded query normalization, field-weighted document modeling, and tiered empty-result avoidance make degraded mode possible and useful. Scheduled drills, versioned index lifecycle management, and user-outcome observability keep the fallback warm as content and traffic evolve. All these enable a dedicated fallback index as a first-class reliability mechanism, instead of something that is only ever invoked in emergencies after the fact.
Persistent inequalities in mathematics achievement continue to represent one of the most persistent forms of educational inequality worldwide. Despite decades of policy reforms aimed at improving inclusive education, students with disabilities, economically disadvantaged students and English learners continue to perform below the general student population in mathematics assessments. These disparities limit not only academic success but also long-term access to higher education, STEM careers and socioeconomic mobility. This study examines variations in mathematics achievement gaps among students with disabilities, economically disadvantaged students and English learners during the 2021-2022 academic year. Using large-scale state-level assessment data, the study applies comparative statistical analysis, including analysis of variance (ANOVA), grade-level trend evaluation and regression modeling, to examine demographic disparities and intersectional effects across educational stages. The analysis captures both between-state differences and within-grade performance patterns to provide a system-level understanding of achievement inequality. The findings reveal substantial and statistically significant disparities across demographic groups and states. Achievement gaps widen progressively as students advance through grade levels, with the most pronounced declines occurring during middle and higher grade levels. Students experiencing intersecting disadvantages, particularly disability combined with economic hardship or language barriers, demonstrate consistently lower mathematics proficiency outcomes when compared with students facing a single challenge. Results further indicate that variations in educational resources, instructional support systems and inclusive teaching practices contribute significantly to observed performance differences. This study concludes that closing mathematics achievement gaps requires investments in inclusive education, targeted instructional support and policies designed to ensure that every student has a fair opportunity to succeed. Therefore, the study recommends that Governments at both federal and state levels should therefore prioritize funding models that direct additional resources to schools and regions where achievement gaps are most pronounced.
The rapid transformation of digital advertising ecosystems has necessitated a shift from traditional pipeline-driven sales models toward partnership-oriented strategic engagement frameworks to achieve sustainable business expansion. This study examines the role of strategic sales management practices in influencing organizational growth outcomes by integrating key constructs such as Pipeline Conversion Efficiency, Relationship Capital Index, Strategic Collaboration Intensity, Data-Driven Targeting Capability, and Cross-Platform Integration Level. Using a quantitative research design and multivariate analytical techniques including correlation analysis, multiple regression modelling, hierarchical cluster segmentation, and Canonical Correspondence Analysis (CCA), the study evaluates the impact of partnership maturity on business expansion indicators such as Revenue Scalability Score, Client Retention Ratio, and Monetization Diversity Index. The findings indicate that relational governance mechanisms and collaborative sales practices significantly enhance advertiser retention and revenue diversification across platform-mediated markets. Furthermore, the integration of analytics-driven targeting frameworks within partnership-based engagement models was found to amplify campaign scalability and long-term monetization performance. These results highlight the importance of adaptive sales leadership approaches that align stakeholder collaboration with data-informed decision-making processes to support sustainable business expansion in dynamic digital advertising environments.
Enterprise analytics platforms across telecommunications, financial services, healthcare, and infrastructure domains increasingly face challenges in maintaining consistency, interpretability, and scalability as they serve diverse user populations and data sources. Traditional design systems work well for consumer apps but fall short for enterprise analytics because they can't handle the complex meanings, performance demands, and governance needs that come with data-heavy platforms. The proposed seven-layer architectural framework treats design systems as socio-technical infrastructure, integrating human-centered design principles with systems engineering practices. The architecture includes basic layers for design tokens and main components, specific layers for data elements and visualization tools, layers for ensuring clear semantics and workflow patterns, and governance systems for lasting effectiveness. This approach demonstrates its real-life applicability in business intelligence tools, network monitoring systems, and financial dashboards, addressing technical challenges such as inter-connecting different systems and improving performance. Additionally, it offers organizational benefits, including enhanced stakeholder alignment, reduced development redundancy, and increased user trust through consistent interpretive frameworks. The layered architecture makes it easier to handle complexity in a systematic way, supporting scalable and comprehensible analytics experiences in complex business environments.
Mission-critical data platforms across financial services, regulatory operations, and enterprise automation now incorporate artificial intelligence at a scale where the accountability properties of the underlying architecture carry direct operational and legal consequences. Systems that influence credit decisions, fraud classifications, compliance reporting, and operational controls must satisfy audit requirements that demand more than reliable outputs; they require that every decision be traceable, explainable, and defensible under examination. Current platform architectures fail this requirement because accountability has been treated as an operational concern rather than a structural one, leaving institutions dependent on post-hoc explanation tools that cannot reconstruct the execution context present at decision time. This article presents a governance framework that treats architectural accountability and decision traceability as first-order design requirements for artificial intelligence platforms operating in regulated environments. The framework is grounded in direct professional experience leading enterprise data platform architecture across high-stakes financial environments and addresses the full accountability lifecycle: decision traceability infrastructure, pipeline governance controls, security and compliance integration, human oversight mechanisms, and model drift management. Each component is developed as an embedded architectural property rather than an operational overlay, producing a platform design in which accountability is sustained by construction rather than enforced by procedure.
Enterprise integration designs that use direct commands are becoming less effective in today's complicated digital world, where interconnected services can cause failures, slow deployments, and more operational problems. Event-driven architecture provides a better structure by replacing direct commands with a reliable and asynchronous Event-driven architecture provides a structured way to replace direct commands between systems with a reliable and delayed sharing of unchangeable updates. However, achieving its benefits requires deliberate architectural discipline, not just casual adoption. This article explores the basic principles, design factors, and rules for the governance that sets sustainable event-driven enterprise platforms apart from ad hoc messaging systems. It addresses domain-oriented event semantics, consumer design, schema governance, and the complementary roles of orchestration in managing distributed business processes. It further explores the scalability engineering, business-aware observability, and security controls that high-volume event platforms demand at enterprise scale. All these aspects together form a clear architectural plan for businesses that want to create integration systems that are strong, easy to track, and able to adapt as the organization and technology change.
Urban Agriculture (UA) has recently attracted growing attention from researchers and policymakers as a strategic component of sustainable urban planning and food systems [1, 2]. This research explores the complex dynamics of coexistence and conflict between urban expansion and agricultural practices in the city of N'Gaous (northeast Algeria)—a small city characterized by urban agricultural pockets deeply interwoven with the urban fabric, which has experienced significant spatial growth and demographic pressure between 1966 and 2024 [3, 4]. Using a mixed methodology combining quantitative spatial analysis (Land Use/Land Cover classification—LULC, and AHP-GIS multi-criteria decision-making model) with qualitative field verification (interviews and participatory mapping), the study aimed to identify and assess areas prone to conflict or coexistence between these two land uses. Findings reveal that N'Gaous lost approximately 239.4 hectares of agricultural land (37%) between 1966 and 2024, driven primarily by demographic growth and urban expansion. The AHP-GIS model (weights: building density 25%, population density 20%) identified three main zones: high-conflict zones (23%), moderate coexistence zones (41%), and high-compatibility zones (36%). Field verification confirmed the model's accuracy. The study also reveals that the continuity of urban agriculture in N'Gaous is linked to the resilience of adaptive farming practices and a shift towards high-value crops. These findings offer a robust methodological framework for decision-makers in small and medium-sized Algerian cities facing similar challenges, emphasizing the importance of recognizing urban agriculture as an essential component of urban resilience and food security [8, 9]. The study contributes to current literature on peri-urban transitions in North Africa and offers methodologically applicable insights for similar contexts.
Identity and Access Management (IAM) controls have become a fundamental element of an information security architecture used to govern privileged access and identity lifecycle, support compliance, authenticate customers, and analyze behavior in a modern, distributed, and interconnected banking environment. Risk-adaptive privilege management systems use Zero Trust principles to provide dynamic entitlement access to payment and core banking systems based on composite risk scoring, including behavioral profiling, transaction limits, and geographic anomaly recognition; this capability removes standing accounts, effectively shrinking an organization's attack surface. Integrated bi-directionally with the Human Resources Information Systems (HRIS), Automated IGA systems orchestrate Joiner-Mover-Leaver processes, accelerating provisioning procedures and preventing orphaned accounts found in other systems. Entitlement segregation is enforced through preventive policy engines that block conflicting entitlement combinations from being assigned․ Automated certification campaigns generate an audit trail to existing compliance regimes such as SOX, PCI DSS, and the GLBA/FFIEC via tamper-obvious chains of custody. Powered by underlying layered authentication mechanisms based on session risk, customer-facing CIAM deployments can also apply behavioral biometrics, device fingerprinting, and liveness detection to find the right balance between frictionless digital experience and security. In high-value trading scenarios, session brokering technologies, UEBA, and AI/ML go beyond authentication and authorization to govern humans and non-humans as privileged actors, optimally moderating risk in tandem with unused privilege detection, role mining, and continuous IAM event anomaly detection.
Introduction: Islamic residential architecture embodies hurma, family cohesion, and stewardship through elements such as courtyards, screened openings, and qibla‑oriented layouts, yet contemporary pressures of globalisation, climate change, and digitalisation have fragmented the related scholarship and obscured how these values are translated into current housing research and practice. Objectives: This article aims to systematically map the intellectual, thematic, and geographical structure of Islamic residential architecture research, clarify how Islamic values and maqāṣid al‑sharīʿa are operationalised within this literature, and develop an integrative theoretical framework—the Islamic Architecture Knowledge Integration Theory (IAKIT)—to guide future studies, design practice, and policy. Methods: A mixed‑method sequential design was applied to 231 Scopus‑indexed publications (1982–2026), combining descriptive and inferential bibliometric techniques (trend analysis, co‑citation, bibliographic coupling, keyword co‑occurrence, cluster modularity, and correlation tests) with qualitative, abductive interpretation of emerging thematic clusters, all framed by a critical realist ontology and post‑positivist epistemology. Results: The analysis reveals sustained growth in output, a socio‑technical disciplinary profile, and four robust thematic clusters—Cultural‑Spiritual Foundations, Environmental‑Performance Integration, Methodological‑Analytical Paradigms, and Heritage‑Innovation Synthesis—that together account for a high proportion of variance and demonstrate increasing integration of sustainability, digital tools, and heritage‑based design logics across regions. These clusters show statistically significant temporal trends and strong links between regional research communities, thematic orientations, and methodological innovation. Conclusions: The findings consolidate a previously scattered body of work, substantiate IAKIT as a four‑dimensional framework that links maqāṣid‑based values with environmental performance and methodological innovation, and highlight emerging opportunities for multidisciplinary, culturally responsive housing strategies in rapidly urbanising Muslim‑majority contexts.
Introduction: Enterprise information systems supporting customer relationship management functions face unprecedented challenges in managing the exponential growth of organizational knowledge assets while maintaining accessibility, accuracy, and regulatory compliance. Organizations generate vast quantities of support documentation, technical specifications, procedural guides, and troubleshooting resources requiring systematic organization and governance frameworks. Knowledge exists in multiple forms and locations throughout enterprises, making systematic management increasingly difficult as organizational complexity grows. Objectives: This article examines architectural patterns and governance frameworks for large-scale Salesforce Knowledge implementations within enterprise information systems contexts. The work addresses planning, design, and implementation challenges across technological, organizational, and social domains encompassing content lifecycle engineering, multi-dimensional governance, multimedia systems integration, and multi-platform service delivery. Methods: The architectural approach integrates workflow automation through Salesforce Flow, role-based access control mechanisms, and cloud computing infrastructure to achieve scalable knowledge delivery. Implementation frameworks address content lifecycle modeling from authoring through archival, organizational governance spanning business units and geographic regions, information security architectures incorporating authentication and authorization mechanisms, and multimedia systems supporting diverse content formats across heterogeneous consumption channels. Results: Empirical evidence from enterprise deployments demonstrates measurable improvements in operational efficiency with agent productivity gains of 40 to 45 percent and customer self-service adoption increases of 55 to 60 percent. First contact resolution rates improve by 25 to 30 percent while training duration reduces by 35 to 40 percent. Enterprise-scale validation through production deployments supporting 50,000 to 75,000 concurrent users and content repositories containing five million to eight million articles confirms architectural scalability. System availability exceeds 99.95 percent while comprehensive audit trails enable regulatory examination efficiency improvements of 40 to 50 percent. Conclusions: The governance model ensures regulatory compliance across General Data Protection Regulation and California Consumer Privacy Act requirements while maintaining content accuracy through automated lifecycle controls. Implementation outcomes validate socio-economic benefits including reduced service costs, enhanced customer satisfaction with Net Promoter Score improvements of 12 to 15 points, and accelerated organizational learning. Strategic implications emphasize holistic architectural approaches balancing technical capabilities with organizational readiness through executive sponsorship and phased implementation strategies.
Riverbank erosion remains a persistent and serious threat to rural communities and agricultural land in the Gangetic plains of India, where highly active alluvial rivers cause continuous lateral migration and bank failure during monsoon seasons. This study evaluates the effectiveness of sectoral dredging as a soft-engineering river training approach through two field interventions in Uttar Pradesh: the Ghaghara River at Village Sirauli Gung, District Barabanki, and the Saryu River at Village Bahadurpur, District Gonda. Pilot channels, referred to as cunettes, measuring 3.8 km and 3.9 km in length and 45 metres in width, were excavated to redirect the river’s main current away from vulnerable concave banks. A hybrid bank stabilisation system combining Geotubes and Balli piling was deployed to induce siltation in abandoned reaches and reinforce the bank toe. Data acquisition integrated LiDAR surveys, high-resolution UAV photogrammetry, bathymetric mapping, and 2D hydrodynamic modelling using HEC-RAS to validate hydraulic performance and sediment transport behaviour. Discharge calculations based on Lacey’s Regime Theory confirmed that the standard 45-metre cunette can convey approximately 472.63 m³/s, while the widened 100-metre channel section achieved a capacity of approximately 1,458 m³/s. Post-intervention numerical simulations and field observations confirmed a significant reduction in near-bank flow velocity to 0.8–1.2 m/s and boundary shear stress to below 15 N/m². Economic evaluation yielded Benefit-Cost Ratios of 1.41:1 for the Ghaghara reach and 1.35:1 for the Saryu reach. In total, 13.46 lakh cubic metres of dredged material were reused in situ for embankment reinforcement and land reclamation, consistent with the National Framework for Sediment Management-2022. The interventions secured the livelihoods of approximately 22,000 residents, protected 8,000 hectares of fertile agricultural land, and generated 15,288 temporary employment days for local communities. These findings present sectoral dredging as a scalable, cost-effective, and ecologically responsible model for flood risk reduction in dynamic braided river systems.
ERP transformations are among the most complex and failure-prone enterprise transformations in modern enterprise life. They consistently show patterns of investment overruns, schedule slippages, and scope shortfalls that earlier risk management frameworks have not been able to manage. The Digital Twin for ERP Transformation framework combines system-of-systems modeling, streaming telemetry, and machine learning-based predictive risk analytics with existing project risk management methodologies to create a dynamic computationally synchronized digital twin of the transformation's risk profile. Guided by ISO 23247 digital twin architecture, NIST AI RMF governance function, and PMBOK risk management processes, operationalize a four-layer digital twin reference architecture of observable program components, integration and telemetry, twin core models, and decision applications. Engineered leading indicators, including change-request entropy, defect acceleration index, backlog flow efficiency, and migration yield, are fed to machine learning classifiers and Monte Carlo simulation engines. This replaces milestone plans with honest, continuously updated uncertainty distributions, which are used to predict probabilistic schedule and cost risk curves. Pathway-specific calibration for greenfield, brownfield, and hybrid ERP migration strategies requires different risk profiles and governance requirements. A system of explainability, bias testing, and human-in-the-loop accountability structures for AI systems to meet AI trustworthiness principles is used to achieve governance obligations. The resulting program risk management framework changes the model of status reporting from after-the-fact to continuous, predictive, actionable intelligence for low-cost intervention before risk materializes, considerably lowering the risk of program failure in complex multi-phase ERP transformation projects.
Enterprise case processing platforms in compliance-driven environments face a challenge that goes well beyond technical performance. These systems must guarantee procedural legitimacy, institutional accountability, and defensible outcomes—properties that conventional enterprise architectures are simply not built to provide. This article develops a formalized architectural framework that treats governance not as a procedural overlay but as something woven into the structure of the system itself. Rather than relying on supervision and post-event auditing to catch problems after they occur, the framework encodes institutional mandates directly into computational execution logic. Four structural components anchor the framework: deterministic lifecycle modeling through finite state machines, hierarchical authorization enforcement aligned with institutional authority, cryptographically verifiable append-only audit logging, and distributed coordination mechanisms that preserve ordered event processing. Drawing from institutional theory, socio-technical systems research, and secure systems engineering, this work positions governance-centered architecture as a compliance-deterministic design doctrine—one that repositions enterprise case processing platforms from administrative utilities into genuine institutional enforcement infrastructure.
The research aims at studying the effect of the Big Five personality traits, Openness to Experience, Conscientiousness, Extraversion, Agreeableness and Neuroticism on attitude and the intention of becoming an entrepreneur among students. Based on a sample size of 200 undergraduate and postgraduate students representing different disciplines, the study uses the descriptive statistics, Pearson correlation and simple linear regression to examine correlations between personality variables and the variables of entrepreneurship. Based on its results, we find out that Openness, Conscientiousness, Extraversion, and Agreeableness impact positively on the variables namely entrepreneurial intentionion and attitude significantly. Neuroticism, on the contrary, shows the high negative effect. Of all the traits, Conscientiousness and Extraversion proved to be the most influential in terms of prediction of entrepreneurial orientation. The observations indicate that the entrepreneurship is psychological in nature and point towards need to ensure that educational institutions train them on personality development and entrepreneurial training to nurture entrepreneurship attitudes and behaviours.In this study, the researcher has added a contribution to the field of personality psychology of entrepreneurship, and also, it can be helpful in practice to those who deal with the education of young people: educators, career counsellors, and policymakers working to develop entrepreneurial skills in younger generations.
Despite the advantages of these technologies, challenges remain due to interoperability issues, redundant activities in the clinic, and workflow delays. The implementation of electronic health records, pharmacy management systems, and telemedicine systems can help healthcare organizations avoid redundancy, streamline document management, and improve operational efficiency. Event-driven microservices allow asynchronous communication and provide scalability and real-time response. With the help of the orchestration layer, healthcare providers can optimize the process of care coordination by automating scheduling, reducing duplicate test orders, and improving the accuracy of clinical records. Additionally, a modular architecture makes the system highly resilient since separate modules can be developed independently without affecting other modules' performance. The proposed workflow orchestration framework aligns with healthcare digital transformation goals, focusing on interoperability, scalability, and patient-centered care delivery.
Modern educational infrastructure encompassing student information systems, library catalogs, and learning management platforms has migrated rapidly to centralized public cloud architectures. While this shift delivers significant operational convenience, it has imposed a systemic "Connectivity Tax" on regional and underfunded school districts. These districts depend on fragile Wide Area Network backhauls that are vulnerable to extreme weather events, physical line breaks, and global cloud provider outages. The Micro-Cloud framework proposes a localized, high-resiliency compute and AI deployment model situated within county boundaries. Built upon lightweight container orchestration and a Hybrid-Edge model that synchronizes with the public cloud during normal operations, the system provides autonomous "Lifeboat" mode functionality during total network isolation. By incorporating localised Small Language Models, the framework ensures essential digital services and AI-assisted tutoring remain available even during prolonged connectivity loss. A simulated 48-hour isolation test demonstrates 98.2% service availability against 0% for a public-cloud-only control group, while a modest hardware investment of approximately USD 3,350 eliminates fate-sharing risks inherent to hyperscale cloud providers.
The deployment of updated large language model-based analytical agents in production environments presents a statistically and operationally complex challenge that conventional software deployment strategies are fundamentally ill-equipped to address. Unlike deterministic systems, where a single test request can expose a defect, stochastic analytical agents require statistical aggregation over many observations to detect quality differences between versions. Existing deployment practices, including canary releases, blue-green deployments, and fixed-window A/B testing, each suffer from distinct failure modes: population confounding, binary risk exposure, or temporal inefficiency. This article proposes a deployment framework that adapts dual-write and dual-read consistency patterns from distributed database migration to the domain of non-deterministic agent serving, integrating them with Wald's Sequential Probability Ratio Test to provide formal statistical guarantees on migration decisions. The framework introduces a Confidence-Gated Migration Protocol that evaluates agent quality across four dimensions, analytical correctness, latency profile, semantic equivalence, and safety, using parallel sequential tests with familywise error control. Phased migration through shadow evaluation and canary deployment, governed by site reliability engineering error budget principles, constrains quality assessment to formal error bounds across all evaluation phases. Simulation results indicate a ninety-six percent rate of detecting regressions, a forty-one percent reduction in mean time to safe migration, and a false alarm rate of less than four percent.
