Optimizing 2D bridge engineering drawing digitization: A comparative study of text recognition tools and development of lightweight post-recognition structured information extraction methods

ABSTRACT As suburban conservation areas expand globally, understanding drivers of community tourism engagement is increasingly vital. By embedding relative deprivation within the prism of sustainability framework, this study investigates residents’ tourism participation intention in Jiuzhenshan National Forest Park, where a former mega-event facility was repurposed for tourism. Using a four-stage mixed-method design, we first applied Random Forest to identify key predictors of participation intention, then employed structure equation modeling (SEM) to test directional relationships among sustainability perceptions, relative deprivation, institutional trust, and residents’ tourism participation intention, and finally conducted interviews to explain underlying mechanisms. Results show that economic sustainability and spatial inclusion positively influence trust and satisfaction, while perceived relative deprivation exerts strong negative effects on trust and participation intention. Qualitative findings further reveal procedural injustice and emotional detachment from governance processes underpin residents’ disengagement, even in contexts where aggregate sustainability outcomes appear favourable. This integrated framework, combining machine learning, causal modelling, and qualitative insights, advances tourism sustainability research by highlighting the importance of spatial equity and trust-building in suburban protected areas. The study offers actionable implications for trust-building and inclusive governance in tourism development and legacy infrastructure reuse.

ABSTRACT Our study examines how frontline managers attempt to manage how street-level bureaucrats make sense of the past when implementing policy reform, using Family Hubs in England as an empirical case. Drawing on sensemaking theory, we identify three modes of managerial sensegiving: discursive, material, and people management practices. We show how: retrospective anchoring limits discursive sensegiving as professionals remember previous reforms; material inertia limits material sensegiving as legacy infrastructures constrain the credibility of new spatial cues; and normative filtering limits people management sensegiving as embedded professional identities lead staff to selectively resist managerial interventions in roles and routines.

This study investigates how Artificial Intelligence (AI) can be systematically leveraged to reinvent smart cities by optimizing legacy infrastructures for greater efficiency, sustainability, and inclusivity. In response to the accelerating challenges of global urbanization, the research examines how AI’s predictive, adaptive, and autonomous capabilities can transform existing systems in transportation, energy management, waste management, public safety, and citizen engagement without the financial and environmental costs of large-scale redevelopment. Employing a qualitative and conceptual research design grounded in a systematic literature review, the study analyzed eighty-five scholarly, institutional, and industry sources published between 2020 and 2025. The findings reveal that AI integration significantly enhances operational performance across all urban domains, reducing traffic congestion by up to 25%, improving energy efficiency by 15–20%, and cutting waste management costs by 10–15%. However, the results also indicate that the benefits of AI deployment are unevenly distributed, heavily influenced by governance capacity, ethical oversight, and citizen participation. The study developed a socio-technical framework that positions AI as a central enabler linking technological innovation with governance, ethics, and social inclusion. This framework emphasizes three interdependent pillars: technological intelligence, institutional capacity, and social inclusion as prerequisites for sustainable urban transformation. The research concludes that the reinvention of smart cities requires not merely the adoption of advanced technology but the creation of accountable, human-centered governance systems that ensure equity, transparency, and trust. Ultimately, the study contributes a conceptual and policy-relevant foundation for reimagining AI-enabled cities as adaptive, resilient, and ethically governed ecosystems. It offers practical insights for policymakers, urban planners, and researchers seeking to balance innovation with sustainability, efficiency with equity, and digital progress with human welfare.

This review explores the transformative role of emerging technologies in railway transportation, emphasizing their contributions to efficiency, safety, and sustainability. With the integration of Artificial Intelligence (AI), the Internet of Things (IoT), Digital Twin technology, and autonomous systems, the railway industry is transitioning towards intelligent and interconnected networks. These advancements address critical challenges such as predictive maintenance, energy optimization, and real-time decision-making, ensuring operational resilience and enhanced passenger experiences. The review methodically evaluates 199 studies, offering insights into regional and temporal trends, and highlighting innovations in automation, safety systems, and sustainability. Additionally, it examines the interplay between advanced technologies and environmental goals, underscoring the importance of green practices and resource efficiency. Despite significant progress, challenges in cybersecurity, regulatory compliance, and legacy infrastructure integration persist. By categorizing literature into thematic domains and identifying critical research gaps, this study provides a comprehensive roadmap for future advancements in intelligent railway systems. Ultimately, it positions emerging technologies as pivotal to addressing contemporary demands and fostering a sustainable and adaptive global railway network.

Rapid urbanisation necessitates reconsidering conventional urban management strategies and embracing creative alternatives for sustainable development due to rapid urbanisation and growing environmental challenges, including climate change, air pollution, and resource scarcity, which are mounting environmental concerns. In this regard, the idea of "smart cities" has surfaced as a strategic framework for enhancing urban efficiency via data-driven governance, digital technology, and intelligent infrastructure systems. This study looks at how digital infrastructure, artificial intelligence, and governance frameworks can be integrated into existing cities to create smart and sustainable urban systems. A comparative case study analysis of Barcelona, Singapore, Dubai, and Baghdad is used in conjunction with a conceptual integration technique. Infrastructure, data systems, artificial intelligence, and governance are the four interconnected layers that make up the study's Smart City Transformation Framework. The results emphasise how crucial data-driven decision-making, adaptive governance, and legacy infrastructure integration are to attaining sustainable urban change. The report also highlights important issues, such as data ethics, energy demands, and sociopolitical limitations. By presenting a multi-layered, context-sensitive model for changing existing cities, the suggested framework advances urban theory.

Introduction: Pharmaceutical manufacturing is undergoing a paradigm shift as it integrates flow chemistry and Industry 4.0 technologies. Continuous flow systems address key limitations of traditional batch processes, offering improvements in safety, scalability, efficiency, and sustainability. This review highlights recent innovations, global case studies, and the implications of adopting flow chemistry in pharmaceutical production. Method: A comprehensive literature survey was conducted, examining peer-reviewed articles, industry reports, and case studies from leading pharmaceutical companies. Particular focus was given to chemical engineering innovations, sustainability metrics, regulatory considerations, and the role of Industry 4.0 tools such as Artificial Intelligence (AI), Machine Learning (ML), digital twins, and predictive analytics. Results: Global case studies from Pfizer, Novartis, AstraZeneca, Aragen, and Adesis demonstrated that flow chemistry enhances reaction control, reduces process time, improves yields, and lowers environmental impact. Indian companies such as Dr. Reddy’s Laboratories, Sai Life Sciences, and Sun Pharma showcased successful adoption, highlighting the technology’s potential in emerging markets. A comparative analysis confirmed significant reductions in the E-factor, Process Mass Intensity (PMI), and carbon footprint. Discussion: While flow chemistry provides clear benefits, challenges remain, including high initial investments, integration with legacy infrastructure, regulatory complexity, and cybersecurity concerns. Collaboration between academia, industry, and regulators is essential to overcome these barriers. Emerging Industry 4.0 technologies offer pathways for real-time optimization, predictive monitoring, and greater supply chain resilience. Conclusion: Flow chemistry, reinforced by Industry 4.0, is enabling agile, sustainable, and efficient pharmaceutical manufacturing. Its adoption supports global health priorities, advances green chemistry goals, and positions the industry for future demands in personalized medicine and decentralized production.

Modern healthcare remains structurally and conceptually fragmented, with profound clinical and policy implications. At its root lies an ontological fracture: the prevailing biomedical model reduces patients to discrete biological systems (organs, biomarkers, and symptoms) detached from the psychological, social, and ecological contexts in which health and illness are experienced. This is compounded by epistemological fragmentation, where medical knowledge is compartmentalized into increasingly narrow specialties, limiting holistic understanding. These philosophical divisions manifest in downstream operational, informational, financial, and policy dysfunctions duplicative testing, misaligned incentives, disconnected care pathways, and population health failures. To address these multilevel fractures, we propose a unified architecture grounded in three interlocking components. First, the Precision and Personalized Population Health (P3H) framework offers a principle-based realignment toward care that is integrated, personalized, proactive, and population wide. P3H addresses the conceptual shortcomings of fragmented care by focusing on the full human trajectory across time, systems, and determinants. Second, General Purpose Technologies including artificial intelligence, biosensors, mobile diagnostics, and multimodal data systems enable the operationalization of whole-person care at scale, especially in low-resource settings. Third, the AI-WHOLE policy framework (Alignment, Integration, Workflow, Holism, Outcomes, Learning, and Equity) provides governance principles to guide ethical, equitable, and context-specific implementation. We argue that this triadic blueprint is particularly critical for Global South nations, where the lack of legacy infrastructure offers an opportunity for leapfrogging toward integrated, intelligent systems of care. Early models illustrate how policy-aligned, technology-enabled care rooted in whole-person principles can yield improvements in continuity, cost-efficiency, and chronic disease outcomes. This manuscript offers a systems-level strategy to overcome fragmentation and reimagine healthcare delivery, not only by refining clinical tools, but by redefining what it means to care for the human being in full.

This paper presents a comprehensive study on maintenance optimization for the cold rolling mill stands at the Libyan Iron & Steel Company (LISCO). While predictive maintenance and simulation are well-established in global steel industry literature, a significant gap exists in applying these methodologies to the specific context of aging mills in North Africa, which face compounded challenges of legacy infrastructure, spare part shortages, and supply chain instability. This study aims to bridge this gap by developing a context-specific, data-driven framework. Building on a comprehensive analysis of historical production data, internal, external, and planned stoppage records, and key reliability metrics, the study identifies major causes of downtime and evaluates their impact on mill availability and productivity. Statistical reliability analysis and discrete-event simulation modeling are employed to support data-driven maintenance planning and resource allocation. The findings highlight critical areas for targeted maintenance interventions that can significantly reduce unscheduled stoppages and enhance overall equipment effectiveness. The primary contribution lies in the integrated application of Pareto analysis, OEE assessment, and simulation to formulate a prioritized action plan tailored to LISCO's operational realities.

Digitalization of the grid promises a sustainable energy system. However, we do not fully understand how innovative technologies are introduced—and challenged—in legacy infrastructures. In this article, I explore how energy industry practitioners in the UK perform alignment work to incorporate novel digital tools into existing systems. I present my argument in three parts. First, a new wave of digitalization challenges an established order of preplanned and highly assured infrastructure engineering with agile and experimental innovation practices. However, this is not happening without pushback from professionals responsible for safety and stability of supply. Second, I show how software innovators and legacy maintainers temporarily leave their roles to align their practices and bridge their respective communities. By arriving at shared understanding of “testing” and “errors,” both groups advance digitalization while remaining cognizant of the unique temporalities and materialities of energy infrastructures. Third, the negotiations are aligned by the shared goal of cybersecurity, which is reframed as a solution to bring together the divergent epistemic cultures found across the industry. What emerges as a result is digitalization enacted on terms shared by innovators and maintainers; a piecemeal, contested, and slow process, resistant to the dominant cultural narrative of urgency and haste.

Digitalization of railway traffic dispatching systems is a key step in the modernization of railway telecommunication infrastructure. This paper presents a case study of the migration from legacy analog technology to a software-centric dispatching platform that integrates digital signal processing, optical fiber transmission, and Internet Protocol (IP)-based network architectures, as implemented in the Serbian railway system. The modernization is performed through an iterative, incremental process: existing analog dispatcher equipment and established operating procedures are preserved, while digital dispatching centers, trackside communication nodes, and radio-dispatching services are introduced gradually. This staged evolution enables high-capacity, noise-resilient communication and seamless interconnection between the old and the new subsystems without disrupting railway operations. The adoption of software-based control and integrated digital signal processing provides modular scalability, real-time system supervision, automated diagnostics, and improved maintainability. One of critical services within the new architecture, the Centralized Call Record- and Message-Archiving System (CCRMAS), provides a centralized platform that captures, secures, and retrieves operational railway communication in real time for monitoring, post-incident analysis, and regulatory compliance. The resulting architecture, deployed within Serbian Railways, establishes a scalable and resilient foundation for future automation, interoperability, and integration within intelligent railway traffic-management environments. Thus, the paper extracts a generalizable hybrid migration architecture model and transferable design principles, supported by deployment artifacts and illustrated through migration scenarios, that can be applied to the modernization of other legacy-intensive railway networks.

The rapid acceleration of digital transformation in the 21st century presents a dual-edged sword for global sustainability efforts. While digital technologies can significantly advance progress toward the United Nations Sustainable Development Goals (UN SDGs), their unchecked proliferation can also exacerbate environmental degradation, social inequities, and governance issues. This study, examines how a nation can strategically align digital transformation with sustainability objectives through the lens of Saudi Arabia’s ambitious Vision 2030, thereby offering a model for other countries navigating similar transitions. Employing a structured narrative synthesis of peer-reviewed research, policy frameworks, official statistics, and institutional reporting, this review evaluates Saudi Arabia’s five-pillar approach: technological resilience, environmental stewardship, social inclusivity, economic diversification, and regulatory agility. Saudi Arabia’s initiatives, such as NEOM, a $500 billion futuristic city powered entirely by renewable energy, exemplify the integration of digital innovation with sustainable urban design, aiming to decouple economic growth from environmental degradation. Key findings reveal transformative initiatives, such as renewable-powered hyperscale data centers, AI-driven precision agriculture reducing water use by 50 %, and inclusive platforms bridging healthcare and education gaps for marginalized communities. These efforts have positioned Saudi Arabia as a regional digital sustainability leader, with its digital economy contributing 15.6 % to GDP and women’s workforce participation in Information and Communication Technology (ICT) tripling since 2018. However, the Kingdom’s experience also underscores systemic barriers, including regulatory fragmentation, legacy infrastructure, and cultural resistance that must be addressed to ensure long-term environmental, social, and economic benefits. The study advocates for a paradigm shift toward digital sustainability, wherein technological innovation is measured not only by efficiency gains but by its capacity to regenerate ecosystems and empower societies. Achieving this shift requires a holistic approach that integrates sustainability principles into every facet of digital strategy, from ethical design and data governance to workforce development and collaborative policymaking. By situating Saudi Arabia’s journey in a comparative global context, this study contributes to the theoretical foundations of digital sustainability and provides actionable insights for policymakers, practitioners, and scholars aiming to harness digital transformation as a force for sustainable development.

Despite over $40 billion invested globally, Carbon Capture and Storage (CCS) captures and stores less than 0.1% of annual global CO 2 emissions, raising serious questions about its efficacy as a climate solution. This review addresses the critical disconnect between modelled expectations and empirical outcomes of CCS, offering a comprehensive, evidence-based reassessment of its technical, economic, and strategic performance. The analysis integrates data from peer-reviewed literature, international reports (IPCC, IEA, Carbon Tracker), and investigative journalism, evaluating both failed and superficially successful projects, including Petra Nova, Gorgon, Sleipner, and In Salah. These case studies expose recurring patterns of cost overruns, suboptimal capture rates, geological uncertainties, and public liability transfer. Projects hailed as “successful” often fall short when scrutinized against durability, scale, and emissions offset claims. While these findings raise valid concerns, this review does not categorically dismiss CCS. Instead, it emphasizes the need for strategic deployment in specific, hard-to-abate sectors where alternatives are limited such as cement production or legacy infrastructure retrofits. Beyond critique, the review explores proven alternatives: renewables, bio-based removals, mineralization; that offer higher scalability and permanence with fewer systemic risks. A predictive evaluation framework is introduced to match/compare capture and mitigation/avoidance technologies to emission contexts using criteria such as CO 2 concentration, energy demand (2.5–8 GJ/tCO 2 ), cost ($50–600/tCO 2 ), scalability, and permanence. Ultimately, the findings underscore that continued reliance on CCS is a high-cost gamble that risks delaying the deployment of truly effective climate solutions; yet, if strategized properly, 2026–2030 could still mark a decisive turning point in determining its role in the energy transition.

The adoption of artificial intelligence (AI) in cybersecurity has become critical as cyber threats become more frequent and sophisticated. However, translating global AI-based cybersecurity approaches into effective national practices remains challenging, particularly in emerging digital economies. This study systematically examines artificial intelligence–based methods for enhancing cybersecurity capabilities in Oman, while critically identifying the technical, organizational, and regulatory challenges constraining their effective adoption. A convergent mixed-methods approach was employed, integrating survey data from 138 cybersecurity practitioners with semi-structured interviews involving 15 experts from government, banking, energy, healthcare, and education sectors. Grounded in the Technology–Organization–Environment framework and Diffusion of Innovations theory, the analysis examined adoption drivers, barriers, and policy implications. Quantitative results show a strong relationship between AI adoption factors and cybersecurity effectiveness (R2 = 0.78). While institutional awareness of AI is high (mean = 4.22 on a five-point scale), realized benefits remain lower (mean = 3.85), indicating implementation gaps. Anomaly detection (73%), Arabic natural language processing (67%), and explainable AI (60%) emerged as the most contextually relevant techniques. Major barriers identified include skills shortages (87%), legacy infrastructure (73%), cost constraints (73%), and regulatory ambiguity (67%). Based on these findings, the study proposes the Oman Cybersecurity AI Framework (OCAIF), a policy-oriented framework that supports phased, context-sensitive AI adoption. The framework integrates technological readiness, workforce development, ethical governance, and sector-specific priorities, offering an empirically grounded roadmap for strengthening cybersecurity resilience in Oman and comparable Gulf Cooperation Council contexts.

Advanced Persistent Threats (APTs) present a paradox in cybersecurity: sophisticated state actors use both zero day exploits and old social engineering tricks, maintaining complex infrastructure while exploiting basic misconfigurations. This study analyzes 60 verified APT campaigns from January 2024 to July 2025, providing an empirical snapshot of current threat actor behaviour, targeting patterns, and detection dynamics. Using a reproducible methodology with clear inclusion criteria based on state backing, persistence, and sophistica- tion indicators, we address four research questions: which actors are active (RQ1), what sectors they target and how this varies by actor (RQ2), which initial access methods dominate (RQ3), and who detects campaigns with what implications for visibility (RQ4). All data are archived in a public repository to enable validation and extension. Our findings reveal concentration among four primary state clusters: Russia (17 campaigns), China (16), North Korea (15), and Iran (9), accounting for 95% of attributed activity. Actor sector relationships show clear patterns: Chinese actors focus on telecommunications and government networks, Russians target diplo- matic infrastructure, North Koreans emphasize financial and cryptocurrency platforms, while Iranian operations cluster around regional events. Social engineering dominates initial access (40%), followed by web/network exploitation (21.7%) and N day exploitation (13.3%), with zero days appearing in only 8.3% of campaigns, chal- lenging assumptions about APT sophistication. Critical to defensive planning, we identify systematic detection gaps from vendor centric discovery that creates predictable blind spots in regions with limited commercial se- curity deployment and sectors using legacy infrastructure. The 18 month persistence of specific actor sector relationships indicates sustained rather than episodic interest, requiring continuous defensive evolution rather than one time responses. These findings require rethinking defensive strategies from isolated organisational responses to collaborative ecosystem approaches. The paradoxical nature of APT operations, advanced yet ba- sic, strategic yet opportunistic, reflects fundamental asymmetries in cyber conflict where attackers need only single successes while defenders must maintain continuous vigilance across expanding attack surfaces. Effective defense requires not just technical controls but coordinated, cross sector frameworks based on observed rather than theoretical threat behaviours. Scope and limitations: Findings reflect publicly reported activity within January 2024–July 2025 and may under-represent restricted disclosures (e.g., Five Eyes and allied operations). We analyse observable evidence in this window, not an exhaustive census. Practical implication: Although actors often chain techniques [Cybersecurity and Infrastructure Security Agency, 2025], we treat the first successful foothold as the decision-relevant initial access because it drives earliest containment and triage; later steps refine rather than replace these priorities.

High-efficiency electric motors represent a core enabling technology for sustainable industrial systems, providing substantial opportunities to reduce electricity consumption, operating costs, and associated greenhouse gas emissions across motor-driven processes. This paper presents a structured synthesis of recent progress in high-efficiency motor technologies within the IE3–IE5 efficiency classes, with emphasis on design innovations in electromagnetic optimization, advanced materials, and thermal management that collectively improve efficiency retention, reliability, and service lifetime under practical duty cycle conditions. Beyond component-level advances, the review analyses how high-efficiency motor–drive systems are being embedded within Industry 5.0 manufacturing environments, where human-centric automation and data-driven intelligence extend motor functionality toward adaptive, condition-aware operation. In this context, the integration of IoT-enabled sensing, AI-based analytics, and digital twin models supports predictive maintenance, real-time condition assessment, fault diagnostics, adaptive control, and duty cycle-responsive energy optimization, thereby improving both energy management and operational resilience. The paper also discusses implementation considerations that commonly constrain industrial adoption, including interoperability with legacy infrastructure, control architecture compatibility, data quality and model robustness, cybersecurity concerns, and lifecycle-oriented sustainability requirements such as material criticality and end-of-life pathways. Representative industrial case studies are synthesized to illustrate typical deployment architectures, observed implementation effects, and recurring technical challenges, together with practical mitigation strategies. This article advances the viewpoint that, under the Industry 5.0 paradigm, the value of high-efficiency motors is evolving from a component-level efficiency upgrade to a cyber-physical enabling asset that shapes lifecycle carbon performance and manufacturing resilience; realizing this shift requires integrated co-design spanning electromagnetics, thermodynamics, information science, and control.

The 2017 NotPetya attack, initially believed to be ransomware, was a destructive digital force that quickly compromised thousands of systems worldwide through supply chains. Its rapid spread brought attention to the vulnerability of corporate networks and industrial supply chains, preying on the global interconnectedness of modern computing environments. This meta-analysis examines the technical and operational attributes of NotPetya and aims to uncover valuable lessons for cybersecurity education. Specifically, this meta-analysis focuses on the unique challenges facing two indispensable industry sectors, logistics and manufacturing, where legacy infrastructure and regulatory requirements amplify the potential consequences of a breach. By integrating cyber-ethics and case studies into curricula, future professionals and institutions can better equip learners to address the evolving complexities of modern cyber threats.

Operational Technology (OT) systems underpin critical infrastructure but face escalating cyber threats, while traditional compliance-based assessments often fail to provide objective, comparable resilience measures. This study aims to develop and validate a quantitative, multi-dimensional Cyber-Resilience Score (CRS) that enables organizations to benchmark and improve their OT cyber posture beyond compliance requirements. A quantitative evaluation model was designed by integrating six measurable dimensions—threat detection, incident recovery, patch management, risk management, system downtime, and compliance—into a normalized and weighted scoring framework. The model was tested using representative datasets from five OT systems across multiple sectors to evaluate resilience differences and operational implications. Results demonstrate that the model effectively distinguishes resilience levels across systems, highlighting detection capability and recovery performance as the most influential contributors to overall cyber resilience. Systems with mature governance and proactive security practices achieved significantly higher CRS values, while legacy infrastructures showed critical weaknesses, particularly in detection and patch management. The CRS model provides a practical governance tool that shifts organizations from audit-driven compliance toward measurable resilience improvement, supporting data-driven investment and risk-management decisions for critical infrastructure protection.

Integrating health informatics, analytics, and digital operations has become essential for improving healthcare performance, strengthening data security, and advancing mental health care delivery in complex and regulated environments. Healthcare systems increasingly rely on fragmented data sources, manual workflows, and legacy infrastructures that constrain clinical efficiency, delay decision making, and expose sensitive patient information to operational and cybersecurity risks. This abstract examines how coordinated digital operations models, supported by health informatics architectures and advanced analytics, can enhance system performance while safeguarding health data and supporting patient centered mental health services. The study adopts a conceptual synthesis of contemporary informatics frameworks, performance analytics models, and digital operations strategies applied across clinical, administrative, and behavioral health domains. Emphasis is placed on interoperable electronic health records, secure data governance mechanisms, and analytics driven performance monitoring to align care delivery with quality, safety, and regulatory expectations. The integration of descriptive, predictive, and prescriptive analytics enables healthcare leaders to identify inefficiencies, optimize resource utilization, and anticipate care demands, particularly within mental health services where continuity, timeliness, and confidentiality are critical. Digital operations models further support standardized workflows, real time reporting, and coordinated care pathways, reducing clinician burden and improving patient engagement. From a data security perspective, embedding privacy by design principles, role based access controls, and continuous monitoring into informatics systems mitigates risks associated with data breaches, unauthorized access, and regulatory non compliance. The abstract highlights how secure analytics platforms can deliver actionable insights without compromising patient trust or confidentiality. For mental health care, integrated digital infrastructures facilitate early intervention, outcome tracking, and personalized care planning while addressing stigma and access barriers through telehealth and remote monitoring solutions. Overall, the paper argues that the strategic convergence of health informatics, analytics, and digital operations offers a scalable pathway to resilient, high performing, and secure healthcare systems. By aligning technological capabilities with clinical objectives and governance frameworks, healthcare organizations can achieve sustainable performance improvements, protect sensitive health information, and deliver more responsive and effective mental health care across diverse populations. These integrated approaches support evidence informed policy, workforce resilience, and long term system adaptability across healthcare landscapes worldwide. Keywords: Health Informatics, Healthcare Analytics, Digital Operations, Data Security, Mental Health Care, Healthcare Performance, Health Information Governance.

ABSTRACT Public Higher Education Institutions (HEIs), especially in resource‐constrained environments, face pressure to modernize IT systems while managing limited budgets and legacy infrastructure. This study explores how enterprise architecture (EA) is applied by ICT leaders in Tanzanian public universities to manage technical debt (TD) under institutional and policy constraints. It provides empirical evidence from low‐resource institutions. It draws on qualitative insights gathered through semi‐structured interviews with ICT managers, system administrators, and academic IT leads in four Tanzanian public universities. A literature review on EA and TD management supported the analysis. Findings reveal that participants view EA as a promising governance tool for mitigating TD by aligning fragmented ICT investments, improving accountability, and reducing duplication in Tanzanian HEIs. The analysis revealed barriers that HEIs face in realizing EA for effective TD management: resistance to change, insufficient technical resources, limited expertise, and difficulty quantifying Return On Investment (ROI). Nonetheless, it was identified that EA could enhance IT governance, agility, and alignment with institutional strategy. For institutional managers, EA provides a structured mechanism to streamline IT governance, optimize digital infrastructure, and align technology investments with long‐term strategic plans. This offers a critical pathway for education leaders facing budgetary constraints and digital transformation demands. The study offers empirically grounded insights into how EA is operationalized within public universities in Tanzania, contributing to the discourse on digital governance and institutional reform in the Global South. The insights are relevant for policymakers aiming to strengthen IT governance and institutional resilience in constrained education systems.