Search Results (242)

Conveyor belt systems are commonly treated as industrial equipment, although their operational value, degradation, risk, and renewal potential are distributed along the route and evolve through identifiable belt sections, splices, inspections, repairs, inserts, and refurbishment cycles. This article redefines conveyor belt loops as digital twin-ready linear assets and proposes a transferable asset management framework integrating three coupled layers: performance and physics, condition data and belt passport, and renewal decisions. The study is designed as a conceptual engineering article based on targeted literature synthesis, structured cross-sector analogy and framework development, rather than as a bibliometric review or a new optimization model. The proposed framework builds on previous work on structure-aware segment renewal by positioning it within a broader data and governance architecture. The article shows that a conveyor digital twin becomes operationally meaningful only when physics-based interpretation, spatially anchored diagnostics, intervention history, operating context, residual value, and auditable decision rules are connected through a persistent belt passport. The framework supports more transparent life-cycle decisions and positions conveyor belt loops as a reference case for infrastructure asset management, condition traceability, and digital twin governance. Full article

The rapid digital transformation of education has increased the demand for intelligent assessment systems and architecture capable of evaluating institutional readiness for technology-enhanced teaching, learning, and workforce development. As educational organizations adopt digital platforms, cloud-based learning environments, and globally connected instructional models, there is a growing need for systematic frameworks that can assess human, technological, and organizational capabilities required for successful implementation. This study proposes a digital capability assessment framework for technology-enhanced educational systems that integrates instructional competency evaluation, technology readiness analysis, infrastructure assessment, and institutional support monitoring within a unified analytics-driven model. The proposed framework consists of multiple assessment components, including digital literacy measurement, technology integration capability analysis, instructional innovation indicators, collaborative learning readiness metrics, and institutional resource evaluation mechanisms. These components are designed to support continuous monitoring of digital transformation initiatives and provide evidence-based decision support for educational planning, resource allocation, and technology adoption strategies. The framework further incorporates analytics and reporting functions that enable stakeholders to identify capability gaps, evaluate implementation risks, and prioritize system improvement initiatives. To demonstrate the applicability of the framework, a pilot assessment was conducted using competency and readiness data collected from instructional personnel within a technology-enhanced educational environment. Analytical results revealed strong capability levels across digital instructional practices, technology-supported curriculum development, online learning delivery, and collaborative knowledge-sharing activities. The assessment also identified infrastructure and support-related constraints that may affect the scalability and sustainability of advanced digital learning initiatives. The proposed framework contributes a scalable architecture for institutional readiness assessment and digital capability analytics within technology-enhanced educational systems. By integrating human capability indicators, infrastructure readiness measures, and organizational support metrics into a unified evaluation model, the framework provides a foundation for intelligent decision-support systems, digital transformation monitoring platforms, and technology governance mechanisms in modern educational ecosystems. Full article

Despite nationwide transparency mandates and expanding digital infrastructure, Chinese local governments adopted online budget disclosure at markedly different times. This study explains this staggered diffusion by integrating the Technology–Organization–Environment (TOE) framework with policy diffusion theory and analyzing 290 prefecture-level governments in China from 2002 to 2023 using discrete-time event history models. The results show that government website development, central government mandates, and peer-city adoption are positively and robustly associated with the likelihood of adoption. By contrast, provincial government disclosure, regional economic development, fiscal pressure, and internet penetration show no stable independent effects in the full models. Interaction analyses reveal, however, that economic development and internet penetration strengthen the association between website development and adoption, while fiscal pressure does not. These findings indicate that local environmental conditions function primarily as enabling conditions that amplify technological capacity rather than as autonomous drivers. The study advances research on fiscal transparency and public-sector innovation by shifting attention from whether governments disclose to when adoption occurs, offering a conditional interpretation of the TOE framework, and demonstrating how vertical authority, horizontal peer dynamics, and digital readiness jointly shape policy diffusion in a unitary governance system. Full article

Background: The rapid adoption of artificial intelligence (AI) in healthcare and management information systems has posed both opportunities and challenges in assessing value across clinical, operational, governance and societal dimensions. Existing healthcare value models are inadequate for the dynamic, data-rich, and ethically complex nature of AI-enabled care and thus necessitate a broader evaluative framework. Objective: This paper proposes an AI-Augmented Healthcare Value Framework (AI-HVF) designed as a multidimensional evaluative lens for assessing value in AI-enabled healthcare across structural, process, outcome, cost, and governance domains. Methods: A narrative, theory-driven literature review was undertaken of healthcare quality frameworks, value-based healthcare, digital health, AI in medicine and public health to identify limitations of existing models and to derive the required dimensions for an updated framework. The dimensions were synthesized into an integrated conceptual model linking structures, processes, outcomes, costs and governance in AI-enabled healthcare. Results: AI-HVF transforms traditional healthcare value models in a data-rich and automated care era. Structural dimensions include digital infrastructure, workforce readiness, learning health systems, and operational efficiency; process dimensions include AI-supported clinical excellence, patient experience, prevention, and explainability; and outcome dimensions go beyond traditional clinical metrics to include equity, safety, continuity, provider well-being, sustainability, and societal value. It also includes real cost accounting and has governance, ethics and trust as an overarching layer that supports accountability, transparency and fairness across all domains. Conclusions: AI-HVF provides a multi-dimensional framework to assess, plan and govern AI in health care at the patient, organization, and system levels. It is intended to enable retrospective evaluation and prospective implementation. The framework offers a foundation for developing future indicators, pilot testing and for comparative evaluation of ethical, equitable and sustainable AI integration in healthcare. Full article

Rapid urbanization has intensified pressure on public services, infrastructure systems, and spatial equity, highlighting the need for integrated approaches that jointly assess service deficits and emerging urban growth. This study proposes an explainable urban analytics framework for identifying service-gap risk and emerging urban patterns using harmonized spatial, service, population, and digital-readiness indicators. The framework integrates an MCP-enabled data harmonization pipeline, composite service-availability and population-adjusted service-stress features, and a Dual-Head MLP architecture that supports shared representation learning across two related prediction tasks. SHAP-based explainability is employed to interpret the relative contribution of service, stress, digital-readiness, and spatial-context features. The framework was evaluated using Saudi district-level data and proxy-based external city datasets to assess cross-city transferability. Compared with classical machine-learning and single-task neural baselines, the Dual-Head MLP demonstrated stronger task-wise predictive performance, while the external evaluation indicated stable but context-dependent transfer across heterogeneous urban settings. The findings suggest that the proposed framework can support urban planners and municipal decision-makers in prioritizing infrastructure investment, identifying underresourced areas, and interpreting early urban transformation patterns. However, the outputs should be regarded as proxy-based decision-support indicators rather than official administrative measures of service adequacy. Full article

Digital transformation has fundamentally reshaped higher education, creating a need for adaptive systems capable of integrating technological infrastructure, learner capabilities, institutional readiness, and learning analytics into a unified decision-support environment. While existing studies predominantly examine online learning from pedagogical or behavioural perspectives, comparatively little attention has been devoted to configurable system architectures that support institutional monitoring and continuous optimization of digital learning ecosystems. This paper addresses this gap by proposing a configurable Digital Learning Capability Assessment Framework (DLCAF), a modular systems framework designed to assess, monitor, and optimize digital learning environments through the integration of infrastructure capabilities, digital literacy, learner motivation, technology acceptance, and institutional performance indicators. The framework employs a layered architecture comprising data acquisition, capability assessment, analytics, decision-support, and feedback modules, enabling flexible configuration according to institutional requirements and educational contexts. To demonstrate the applicability of the proposed framework, a survey involving 220 students from 27 study programs across Croatian higher education institutions was conducted during the COVID-19 digital transition. The empirical findings serve as an application case for validating the framework and illustrating how learner perceptions, technical constraints, institutional support, and digital readiness can be systematically incorporated into an adaptive decision-support process. The results indicate that technical infrastructure, learner motivation, digital competencies, communication quality, and institutional support collectively influence the effectiveness of digital learning environments. The proposed framework transforms these heterogeneous indicators into actionable institutional intelligence that supports evidence-based planning, continuous monitoring, and targeted intervention strategies. By repositioning digital learning evaluation as a systems engineering problem rather than solely an educational assessment exercise, this work contributes a reusable and extensible framework that can be deployed across diverse higher education environments. The architecture provides a foundation for future integration of artificial intelligence, learning analytics, predictive modelling, and adaptive recommendation mechanisms, supporting the development of intelligent digital learning ecosystems capable of continuous improvement and institutional decision support. Full article

The integration of information technologies into operational technology (OT) environments that encompass industrial control systems (ICSs), Supervisory Control and Data Acquisition (SCADA) systems, and critical infrastructure such as water and wastewater management facilities has introduced significant cybersecurity vulnerabilities alongside urgent questions of ethics and accountability. The existing digital forensic ethics frameworks were developed for enterprise IT contexts and provide no specific governance for investigations conducted within safety-critical OT environments, where a forensic misstep can disrupt water delivery or disable safety controls. This paper employs a four-stage qualitative research design that consists of a systematic literature review, ethical framework analysis grounded in principlism cyberethics principles, forensic process mapping, and multi-case analysis of five documented real-world incidents to address this gap. The paper identifies and categorizes six primary ethical challenges that are unique to OT/ICS/SCADA digital forensics, maps each to the investigative phase at which it arises, proposes theWater ICS SCADA Ethical Digital Forensics (WISE-DF) framework as a six-phase principled investigation model with ethical safeguards embedded as mandatory procedural gates, and provides actionable recommendations for investigators, organizations, and policymakers. Validation employs three complementary approaches: standards-alignment against the National Institute of Standards and Technology (NIST) Special Publications (SP800-82r3) and Internal Report (IR 8428), the AmericasWater Infrastructure Act of 2018 (AWIA 2018), and the Environmental Protection Agency (EPA 2024); counter-argument testing of each recommendation; and Daubert-consistency evaluation assessing whether WISE-DF compliance produces forensic evidence that is capable of meeting U.S. federal admissibility requirements. Although the Daubert standard applies specifically to U.S. federal proceedings, the ethical framework and WISE-DF gates are designed to be generalizable across jurisdictions. Water and wastewater management serves as the primary illustrative case domain; the framework is designed to be generalizable across OT sectors. Full article

Smart cities have emerged as a strategic approach to enhancing urban sustainability through the integration of digital technologies, intelligent infrastructure, and data-driven governance. However, limited research has comprehensively examined how technological, institutional, governance, and contextual factors interact to influence sustainability outcomes in industrial-city settings, particularly in Saudi Arabia. This study develops an integrated conceptual framework for sustainable smart industrial cities through a qualitative research design based on a systematic screening and synthesis of the literature, complemented by a contextual analysis of the Dammam Metropolitan Area (DMA) as an illustrative urban-industrial case under Saudi Vision 2030. A total of 86 articles, reports, and website materials were identified and screened, of which 49 sources met the inclusion criteria and were synthesized to develop the proposed framework. The analysis examined five interrelated components: smart-city applications, institutional governance, implementation conditions, contextual barriers and opportunities, and sustainability outcomes across three dimensions: environmental, social, and economic. The findings suggest that the effectiveness of smart-city applications depends not only on technological innovation but also on institutional governance, digital readiness, stakeholder participation, integrated planning, and supportive regulatory environments. The study further identifies fragmented governance, limited institutional coordination, cybersecurity risks, and high infrastructure costs as key implementation barriers, while highlighting opportunities associated with Saudi Vision 2030, digital-transformation initiatives, and circular-economy principles. The principal contribution of the study is the development of an integrated conceptual framework that explains the interactions among these five components and provides a theoretical foundation for future empirical validation, as well as conceptually informed guidance for policy development and planning in Saudi Arabia and comparable industrial-city contexts. Full article

This study proposes a sequential Hybrid GP-ANN architecture based on the concept of autonomous feature engineering for multi-output performance prediction of single-phase induction motors, serving as a high-fidelity surrogate model ready for digital twin integration. The required high-resolution motor dataset (88,375 samples) was generated using a parametric sweep approach with Ansys RMxprt. In the proposed architecture, Genetic Programming (GP) is not positioned as a final predictor but as an analytical filter that discovers hidden physical relationships in raw input data and converts them into super features, thereby structurally eliminating the polynomial explosion risk and scale sensitivity that arise when raw data are modelled directly. The nonlinear physical relationships discovered autonomously by GP are added to the network input matrix as mathematical vectors, breaking the black-box structure of standard artificial neural networks and transforming it into a physics-inspired grey-box model. The nonlinear terms discovered by GP are fed into the network after independent Z-score normalisation to preserve gradient stability. To comprehensively evaluate this framework, its predictive performance is benchmarked against industry-standard machine learning algorithms, including Random Forest (RF), Support Vector Regression (SVR), and Extreme Gradient Boosting (XGBoost). Results on variation-based unseen test data show that the hybrid model achieves competitive prediction accuracy relative to XGBoost—matching or exceeding it for Output Torque and Input Current, and remaining broadly comparable for Output Power, though XGBoost achieves substantially lower RMSE for Efficiency and Power Factor—while additionally offering transparent mathematical traceability, reducing error rates (RMSE) by 42.1% to 66.1% per parameter compared to the standard ANN. The model achieves an $R^2$ score of 0.9879 for the power factor parameter, where conventional approaches struggle. To rigorously validate the interpretability of this grey-box architecture, a global Permutation Feature Importance (PFI) analysis was conducted, showing that the GP-derived super features collectively account for 61.46% of the decision logic, outweighing the combined contribution of the raw inputs (38.54%). Furthermore, the autonomous feature engineering layer is found to reduce the learning burden on the ANN, allowing it to converge to a substantially lower error floor within the same fixed epoch budget. With an online inference time below 0.02 ms, the proposed architecture offers a robust methodological infrastructure for sustainable motor digital twins through a balanced trade-off between prediction accuracy and computational efficiency. Full article

Hydrogen (H₂) has emerged as a promising sustainable energy vector due to its scalability, high energy density, and its ability to enable sector coupling across electricity, heating, transportation, and industry. There remains a huge technical challenge to overcome. The economic implications of low round-trip efficiency, high capital costs, the limited lifespan of fuel cells and electrolyzers, and infrastructure constraints on H₂’s relative competitiveness have not been comprehensively studied. In a comparative assessment against other storage options such as batteries, pumped hydro, and compressed air energy storage (CAES), we highlight the potential of H₂ as a grid-scale storage solution. The novelty of this paper is that it compares H₂ as a competing option with other storage technologies and highlights its unique suitability for seasonal and grid-scale applications where others fall short. The paper also discusses the technological opportunities for AI and other digital technologies in the H₂ grid. Unlike general reviews, this work emphasizes the engineering performance of H₂’s production cost and economic viability, electrolyzer technology maturity, infrastructure readiness, safety and lifecycle considerations, and provides critical synthesis and implications. With this, the paper extends beyond the theoretical capacity for H₂ and gives an engineering-focused analysis that informs the drive toward sustainable and resilient power grids. Full article

The accelerating digital transformation of urban economies is reshaping labor markets at unprecedented speed, generating skills mismatches, employment volatility, and widening inclusion gaps that current smart city governance frameworks are insufficiently equipped to address. While the smart city literature has advanced substantially in the areas of digital infrastructure, mobility, and e-government services, the governance of labor market transitions in data-driven urban environments remains conceptually underdeveloped. In particular, no integrated analytical framework currently links smart city governance, labor market intelligence, and workforce resilience into a coherent tool for assessing urban preparedness for technology-driven employment change. This study addresses that gap by developing the Urban Workforce Readiness Framework (UWRF)—an integrated conceptual model designed to evaluate how prepared urban labor markets are for accelerating digital and technological transformation. Methodologically, the framework is constructed through a structured synthesis of peer-reviewed scholarship published between 2015 and 2025 across five domains—smart city governance, labor market regulation, human capital development, workforce resilience, and data-driven public administration—complemented by a thematic review of policy documents issued by the OECD, ILO, European Commission, and World Bank. On this basis, the UWRF identifies five interdependent dimensions of urban workforce readiness: (i) digital infrastructure capacity, (ii) labor market intelligence and analytics, (iii) workforce skills adaptability, (iv) institutional governance capacity, and (v) social inclusion mechanisms. A multi-criteria operationalization is proposed, enabling comparative diagnostic assessment across cities and supporting evidence-based prioritization of policy interventions. The analysis demonstrates that institutional governance capacity and real-time labor market intelligence function as critical mediators within the system: in their absence, even substantial investments in digital infrastructure fail to produce resilient, inclusive, or sustainable labor market outcomes. Theoretically, the study extends data-driven governance scholarship beyond service delivery into the domain of workforce management, thereby integrating three traditionally separate research streams—smart city studies, labor market governance, and digital public administration—under a single analytical architecture. Practically, the UWRF provides policymakers, municipal authorities, labor market institutions, and urban planners with a structured diagnostic instrument for aligning digital transformation strategies with sustainable and equitable employment outcomes, and offers a replicable foundation for future empirical validation across diverse urban contexts. Full article

The accelerating transition toward renewable electricity generation has elevated grid-scale electrochemical energy storage from an ancillary grid service to a foundational infrastructure requirement. This review provides a comprehensive account of rechargeable battery technologies for stationary grid applications, spanning advanced lithium-ion systems, sodium-ion and post-lithium multivalent chemistries, vanadium and organic flow batteries, solid-state architectures, and high-energy-density future systems such as lithium-sulfur and metal-air cells. The techno-economic context of grid-scale storage is systematically examined, including performance metrics, market drivers, and regulatory frameworks. Each battery chemistry is analyzed with respect to electrochemical mechanism, cycle life, energy density, safety profile, material availability, and commercial readiness. Non-electrochemical storage technologies are discussed as system-level alternatives. Battery safety engineering, thermal management system design, thermal runaway mechanisms and prevention, and failure containment strategies are examined in depth, followed by analysis of critical material supply-chain vulnerabilities, life-cycle assessment, and recycling pathways. The expanding role of artificial intelligence, machine learning, and digital twin frameworks in optimizing performance and enabling predictive maintenance is reviewed. Key challenges, including material bottlenecks, manufacturing scalability, long-duration storage gaps, and the absence of harmonized performance standards, are identified, and the review concludes with a techno-economic roadmap toward cost-competitive, resilient, and low-carbon grid storage. Full article

South Korea has achieved internationally recognized digital government capacity and platform-based service integration, yet recent privacy and cybersecurity incidents reveal growing risks to digital trust. This article examines how national governance models shape digital trust risk under conditions of platform dependency, large-scale data breaches, and artificial intelligence (AI)-enabled cybersecurity threats. Drawing on a comparative socio-technical systems perspective, the study analyzes three digitally advanced but institutionally distinct cases: South Korea, Estonia, and Taiwan. South Korea is examined as a reactive platform-accountability model, Estonia as an architecture-based data-auditability model, and Taiwan as a civic-resilience and joint-defense model. Rather than applying a formal quantum-probability model, the article uses the QP-Gov framework as a bounded analytical lens for interpreting context sensitivity, latent trust, accountability visibility, and abrupt trust-risk shifts. Methodologically, the study adopts a most-different systems design and combines structured profile analysis, case tracing, and an evidence matrix. The comparison focuses on five dimensions: digital density, data concentration, accountability visibility, incident responsiveness, and AI-era readiness. The findings show that digital trust depends not simply on technological sophistication, but on whether citizens can observe how data are accessed, how breaches are handled, how responsibility is allocated, and whether institutions learn from incidents before trust damage becomes systemic. The analysis suggests that Korea’s reactive model demonstrates strong post-incident investigative and regulatory capacity but remains vulnerable when accountability becomes visible only after major breaches. By contrast, Estonia and Taiwan illustrate alternative mechanisms of preventive auditability and resilience-based preparedness. The article concludes by proposing a sequenced policy pathway for South Korea, including citizen-facing data-access logs, systemic platform duties, breach-consequence dashboards, AI-agent audit trails, zero-trust infrastructure, and independent digital trust oversight. Full article

Background: Inequitable access to clinical trials persists in blood cancers despite ongoing equity, diversity, and inclusion (EDI) efforts. Despite the critical role of clinical trials in improving survival and outcomes, recruitment remains suboptimal, limiting patient access to potentially life-saving therapies. Practical and scalable approaches are therefore needed to address the non-medical barriers that hinder patient readiness upstream of enrolment. Methods: Myeloma Canada led a national, multi-phase initiative using human-centred design (HCD) to operationalize EDI in clinical trials. Following an initial systems level workshop, the two-day Phase 0 workshop used a HCD approach that convened a purposively selected multidisciplinary group of stakeholders to co-design operational solutions for non-medical barriers affecting trial participation for patients. Given the use of purposive sampling, the results should be interpreted as reflecting a balanced range of diverse, informed perspectives across the Canadian clinical trial ecosystem. Results: Participants identified persistent cultural, logistical, financial, and linguistic barriers, along with fragmented awareness of available supports. Across diverse personas and care settings, all groups independently converged on a human-centred, equity-focused pre-trial navigation model supported by simple digital tools, including AI-enabled infrastructure drawing on curated resources from validated sources with appropriate governance, privacy, and oversight. Digital tools were proposed to support, rather than replace, human support and to align with existing health system realities. Conclusions: This hypothesis-generating work proposes a feasible, sustainable, and scalable equity-embedded, protocol-agnostic navigation framework. Its external hub-and-spoke structure can reduce non-medical barriers, strengthen trial access and accrual, and enhance representativeness. Pilot implementation that assesses feasibility, uptake, workflow impact, equity effects, and implementation burden is warranted. Full article

This systematic review examines the impact of flexible work arrangements and hybrid work on small- and medium-sized enterprises, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses 2020 framework. A total of 35 peer-reviewed studies published between 2016 and 2025 were analyzed from Scopus and Web of Science databases to identify global patterns, enabling factors, and organizational outcomes of workplace flexibility in small- and medium-sized enterprises. The findings reveal that flexible work arrangements and hybrid work have evolved from temporary responses during the COVID-19 pandemic into long-term organizational strategies in many contexts. Three key themes emerged from the analysis: workplace flexibility and employee outcomes, digital transformation as an enabling capability, and leadership as a driver of organizational resilience. Overall, the reviewed studies indicate that flexible work arrangements are generally associated with improvements in productivity, employee well-being, innovation, and organizational adaptability. However, these outcomes vary significantly across regions and are strongly influenced by digital readiness, leadership practices, and institutional support. Small- and medium-sized enterprises in developed economies demonstrate higher levels of digital maturity and more structured hybrid work adoption, while those in developing economies face constraints related to infrastructure, skills, and resources. The study concludes that flexible and hybrid work should be understood as integrated organizational strategies requiring strong digital capability, adaptive leadership, and supportive policy environments to ensure sustainable performance in small- and medium-sized enterprises. Full article