Search Results (688)

Digitalisation is transforming organisational practices, making digital readiness essential for strategic planning. However, customised digital maturity tools for the Irish Architecture, Engineering, Construction, and Operations (AECO) sector remain limited. This paper presents the development and validation of a Digital Maturity Gap Analysis Toolkit (DMGAT) for the Irish AECO sector. The toolkit assesses digital maturity across three dimensions—people, process and culture; technology; and policy and governance—covering 16 sub-dimensions and 69 assessment questions. Unlike existing tools such as the BIM Maturity Matrix, VDC BIM Scorecard, and Maturity Scan, the DMGAT uniquely integrates ISO 19650 maturity stages with a comprehensive maturity level matrix across three key dimensions, offering a customised, industry-specific assessment for the Irish AECO sector that combines structured benchmarking with actionable gap analysis. The toolkit supports gap analysis by comparing an organisation’s current maturity profile with the detailed descriptors of higher maturity levels (maturity level matrix), thereby enabling prioritised and context-specific improvement planning rather than pursuit of a uniform maximum level. The study uses a mixed-methods approach within a Design Science Research (DSR) framework, developing the tool across six phases: literature review, defining dimensions and key performance indicators (KPIs), prototype development, testing, refining and finalisation, and deployment for practical application and empirical evaluation within real organisational contexts in the Irish AECO sector, demonstrating its use as an operational diagnostic and learning tool. Alpha testing by the organisational research team refined structural enhancements including maturity stages, KPIs, and maturity matrix. Beta testing with 20 Irish AECO organisations confirmed the toolkit’s relevance, scope, and coverage. Participants highlighted its clarity and industry alignment, while suggesting minor improvements in wording, visuals, and support materials. This study concludes that DMGAT is a useful resource for informed decision-making and digital innovation in the Irish AECO sector. Full article

Mechanical pretreatment techniques are essential for overcoming lignocellulosic biomass recalcitrance in emerging biorefineries. This review critically synthesizes advances from 2020 to 2025 across fundamental mechanisms, hybrid technologies, energy efficiency, Life Cycle Assessment, and industrial scalability. The analysis reveals that effective pretreatment targets supramolecular modification—defect generation in cellulose crystallites and the creation of reactive sites—beyond simple particle size reduction. Impact–shear regimes prove most effective for fibrous materials. Hybrid approaches are examined: mechanocatalysis enables solvent-free depolymerization, while mechanoenzymatic technologies achieve hydrolysis without bulk water, though enzyme denaturation under mechanical stress remains unresolved. Energy consumption is the primary upscaling barrier, with Life Cycle Assessment identifying electricity use as the dominant environmental hotspot and emphasizing burden per unit of final product as the critical metric. Technology Readiness Level assessment provides a strategic framework: continuous extruders and mills are industrially mature for bulk applications, while high-intensity batch devices are suited for high-value coproducts. A research agenda prioritizing mechanistic understanding, hybrid process engineering, feedstock diversification, and embedded sustainability assessment is proposed. Full article

This systematic review evaluates recent scientific and technological advances in water quality monitoring and pollution alarms for ports, based on records retrieved from seven databases following the PRISMA protocol. A total of 414 documents were screened, resulting in 141 articles (TRL 3) and 56 patents (TRL 4–5). Bibliometric, patentometric, and thematic analyses were conducted using Bibliometrix and ORBIT^®. Results show sustained growth in both academic and technological outputs, with a patent Compound Annual Growth Rate (CAGR) of 32%, compared with 13% for scientific publications, indicating accelerated translation from research to innovation. The conversion rate from scientific research to patenting increased from 14% (2010–2015) to 47% (2020–2023). Analysis of patent legal status reveals that 52% of patent families remain valid (48% granted; 4% pending), while 33% are lapsed, 13% revoked, and 2% expired, reflecting the dynamic and emerging character of the field. Technological ownership is highly concentrated, with China accounting for nearly all active patents, whereas scientific production is more geographically distributed. Thematic analysis identifies four main scientific clusters: environmental monitoring, chemical pollutants, seashore hazards, and eutrophication. The main technological domains of the patents are analysis of biological materials, control, and environmental technologies. Emerging areas of focus at TRL 3 and TRL 4–5 include microplastics, climate-change impacts, aquaculture risks, real-time sensing, IoT-enabled platforms, machine-learning analytics, autonomous monitoring systems, and bioindicator-based early-warning tools. This review provides a quantitative roadmap to support sustainable port operations, coastal ecosystem protection, and progress toward multiple synergistic United Nations Sustainable Development Goals (SDGs). Full article

A review of morphing actuation systems in relation to rotary-wing aerial platforms is presented. The research highlights an inadequate maturation of rotary actuation systems, characterised by a scarcity of (1) comprehensive full-scale experimental research relative to non-rotary (fixed-wing) systems, (2) techniques used for rotary actuation systems and (3) implementation of full-chord morphing systems, with existing research only utilising partial-chord actuation techniques. Additionally, another notable shortcoming is presented to be the lack of comprehensive proportional investigation in the proposed five-step development process for rotary actuation designs. A comprehensive critical review is offered, covering the following challenges of progressing through this development process for rotary actuation systems from conceptual design to production: (1) numerical and computational studies, (2) small-scale wind-tunnel testing, (3) full-scale wind-tunnel testing, (4) demonstrator, and ultimately (5) fabrication for industrial implementation. The review examines several existing rotary actuation systems, including (but not limited to) leading-edge, trailing-edge and Gurney flaps; active twist; chord extension; variable span and camber systems. Comparisons are made between rotary morphing actuation systems and their non-morphing counterparts, highlighting the distinct difficulties encountered by rotary-wing systems due to the more complex and challenging operational conditions found in rotorcraft. The review reveals that a significant portion of existing research on rotary-wing systems has focused only on early-stage development, including computational modelling and sub-scale wind-tunnel experiments, underscoring the necessity for more comprehensive full-scale testing and prototype evaluation given that only a small number of studies have progressed to full-scale wind-tunnel testing or actual prototype evaluation, with only one example identified as having been tested on a production helicopter. In addition, a comparative Technology Readiness Level (TRL) assessment is presented for both rotary-wing and fixed-wing morphing actuation systems, enabling a structured evaluation of relative technology maturity, experimental validation depth, and proximity to operational implementation. Building upon this assessment, a morphing Actuation Concept-Transfer Feasibility (ACTF) study is also provided, examining the potential for adapting mature fixed-wing morphing actuation technologies for application in rotary-wing environments, while identifying the key structural, aerodynamic, and operational constraints that currently limit direct technology transfer. This study addresses and proposes opportunities for a novel rotary actuation system design and concludes by suggesting the potential for future research on more effectual systems to include full-chord configuration over larger spanwise blade footprints with innovative actuation mechanisms that could be utilised and progressed through all development stages from numerical studies to full-scale fabrication. Full article

Background/Objectives: Women’s health has historically lagged behind other medical specialties in transformative innovation, despite significant technological advances in adjacent fields. In this collection of papers, we examine the current state of innovation in women’s health and maternal–fetal medicine, identify barriers to transformation, and propose strategies for accelerating breakthrough developments. This paper presents an overview of multiple forces and their often-competing relationships that influence the environment in which advances in multiple areas of healthcare have had to navigate to enter mainstream practice. An understanding of these forces is essential to explain why some new technologies are readily deployed into clinical practice while others take many years to be adopted. Understanding the entire “echo-system” around any specific technology provides a much fuller understanding of how any individual advance can make its way into actual utilization. Methods: We synthesized current literature on innovation in women’s health, analyzing technological advances in artificial intelligence, precision medicine, non-invasive diagnostics, and surgical robotics. We examined patterns of innovation adoption and barriers to implementation across multiple domains. Results: Several key areas presented in this paper and the following show promise for transformative change: artificial intelligence (AI)-driven diagnostics achieving expert-level performance in prenatal screening, precision medicine approaches transforming genetic disease management, and non-invasive monitoring technologies revolutionizing maternal–fetal care. However, systemic barriers including regulatory complexity, liability concerns, and institutional inertia continue to limit widespread adoption of numerous breakthrough technologies. Conclusions: The convergence of multiple technological advances, particularly artificial intelligence and precision medicine, positions women’s health for unprecedented transformation. Success requires fostering innovation-ready environments, embracing systems-awareness approaches, and maintaining focus on human-centered care while leveraging technological capabilities with continual feedback and course corrections. Full article

This study analyzes how sustainability practices and digitalization jointly influence the financial performance of European industrial firms, emphasizing differences between Western and Eastern Europe. The empirical analysis relies on a large multi-country panel dataset and employs fixed effects regression models with robust standard errors to account for unobserved firm-specific heterogeneity and common time shocks. Environmental sustainability is captured by the environmental component of ESG scores, digitalization is measured by digital investment intensity, and financial performance is proxied by return on equity (ROE). The findings indicate that stronger environmental practices are positively associated with profitability across the full sample. Digital investment intensity also has a positive and statistically significant effect on ROE. Importantly, the interaction term between environmental performance and digitalization is positive and significant for Western European firms but not for the full sample, suggesting that the relationship between environmental practices and financial performance may vary with the level of digital investment under specific regional conditions. However, the results reveal substantial regional heterogeneity. The positive effects of environmental practices, digitalization, and their interaction are primarily driven by firms in Western Europe, whereas the relationships are weaker and statistically insignificant in Eastern Europe. These findings underline the complementary role of digital transformation and the importance of institutional and technological readiness. Full article

Adaptive learning at scale requires explicit handling of uncertainty and information flow across diverse educational technologies. This paper proposes a TOGAF-conformant enterprise architecture for the University of Tabuk (UT) that embeds entropy- and uncertainty-aware requirements from the outset and aligns them with institutional goals in teaching, research, and administration. Using the Architecture Development Method (ADM), we map information-theoretic requirements to architectural artifacts across the architecture vision, business, information systems, and technology domains; formally specify core entropy-informed observables, including predictive entropy, expected information gain, workflow variability entropy, and uncertainty hot-spot severity; and define semantic and metadata standards for their near-real-time computation. These indicators are positioned explicitly across the TOGAF domains: business architecture identifies where uncertainty matters, information systems architecture defines the computable data and application representations, technology architecture operationalizes secure and scalable computation, and later ADM phases use the resulting metrics for prioritization and governance. The architecture also establishes governance that ranks initiatives by their expected uncertainty reduction through Architecture Review Board (ARB) decision gates. We address three research questions: (R.Q.1) how to design a TOGAF-conformant architecture for UT that natively encodes uncertainty-aware requirements and aligns with institutional needs; (R.Q.2) how to integrate dispersed data, achieve semantic harmonization, and deliver analytics-ready streams that support information-theoretic indicators for personalization without delay; and (R.Q.3) how to embed IT demand planning in opportunities and solutions and migration planning using uncertainty reduction and expected information gain as prioritization criteria. The resulting architecture offers a university-wide foundation for adaptive learning: it unifies learner and system interaction data under governed schemas, supports low-latency analytics, and formalizes decision processes that treat uncertainty as a primary metric. Though learner-level operational validation is future work, the design establishes the technical and organizational foundations for responsible, large-scale deployment of entropy-driven learner modeling, content sequencing, and feedback optimization. Full article

This study examines the use of artificial intelligence (AI) in organizational decision-making processes (DMPs) related to investments in renewable energy sources (RESs). The research addresses the gap between AI’s technological capabilities and its actual application in investment practice. An empirical two-stage survey was conducted in 2025, and a comparative analysis was conducted to assess the stability of attitudes toward AI adoption. The findings indicate a low level of practical implementation of AI tools in investment decision-making, despite a clear perception of their potential usefulness, particularly for risk analysis and improving decision objectivity. Organizations tend to perceive AI primarily as analytical support rather than an autonomous decision-making mechanism. The results also reveal a persistent level of uncertainty and hesitation associated with trust in AI systems. Comparative analysis confirms that these attitudes remain stable across research stages, suggesting structural rather than temporary barriers to adoption. This study demonstrates that limited adoption of AI in renewable energy investment decisions results mainly from organizational readiness and trust-related factors rather than technological constraints. The paper contributes empirical evidence on the behavioral and organizational determinants of AI implementation in the context of sustainable energy transition. Full article

Digital Twin (DT) technologies are widely discussed in the context of Industry 4.0 and advanced manufacturing; however, their role in supporting the sustainability and survival of academic spin-offs remains underexplored. This paper argues that, particularly in peripheral and resource-constrained innovation ecosystems, Digital Twins should be understood not merely as optional technological enhancements but as strategic capabilities that support sustainable technology commercialization in early-stage, research-driven ventures. Building on literature on academic entrepreneurship, technology commercialization, digital innovation, and regional innovation systems, the study develops a conceptual framework that positions Digital Twins as entrepreneurial infrastructures linking scientific outputs to market readiness through three interrelated mechanisms: the reduction in technological uncertainty, the acceleration of market validation, and the enhancement of organizational learning and strategic adaptability. Extending beyond conceptual development, the paper proposes a staged Digital Twin adoption roadmap aligned with Technology Readiness Levels, offering a practical pathway for integrating DT capabilities across venture maturation phases while strengthening investor readiness and commercialization outcomes. The analysis further connects DT-enabled experimentation with sustainability objectives by demonstrating how virtual testing, digital validation, and data-driven learning support capital-efficient, resource-conscious, and resilient innovation processes. By integrating theoretical insights with operational guidance, this conceptual study contributes to research on technology transfer, deep-tech entrepreneurship, and sustainability-oriented innovation by proposing a framework that may guide future empirical investigations of Digital Twin adoption in academic spin-offs. The framework also offers actionable implications for spin-off founders, university technology transfer offices, and policymakers seeking to foster resilient and inclusive innovation ecosystems. Full article

Urban blue–green infrastructure (BGI), comprising vegetation and aquatic elements, is fundamental to city resilience and climate adaptation. Effective BGI management necessitates high-resolution, spatially accurate data for which Unmanned Aerial Vehicles (UAVs) have emerged as versatile monitoring tools. This study provides a critical synthesis and analytical evaluation of UAV-based technologies for BGI management from 2018 to 2025. Following a PRISMA-guided methodology, the review evaluates dominant research themes, sensor technologies (RGB, multispectral, thermal, LiDAR, and water and air quality sensors), and analytical methods. Departing from traditional descriptive reviews, this study appraises the operational maturity of these technologies using an adapted Technology Readiness Level (TRL) framework. The analysis identifies a significant “maturity gap” between standardized structural mapping (TRL 9) and experimental functional assessments of environmental conditions (TRL 4–6). Notably, the article includes a detailed analysis of specific UAV platforms and sensors, providing specifications of technological capabilities. By identifying critical technical, regulatory, and economic bottlenecks, this review provides a robust, evidence-based foundation for the deployment of drones in enhancing urban resilience and sustainable environmental governance. Full article

The integration of immersive educational technologies into medical education has attracted growing attention owing to their potential to improve the learning of complex anatomical structures and specialized terminology. This study investigates the use of zSpace technology as an interactive, learner-centered instructional tool for teaching the human respiratory system to undergraduate students in Nursing, Midwifery, and Physician Assistant programs. A structured pedagogical framework combined prior theoretical instruction in anatomy and Latin medical terminology with a zSpace-based practical learning activity was used. After the workshop, the students completed a survey evaluating perceived learning effectiveness, student engagement, and the quality of three-dimensional (3D) visualization. Data from 34 participants were analyzed using descriptive statistics and reliability analysis. The results indicated high levels of student satisfaction regarding the clarity, anatomical detail, and educational value of the immersive 3D models, along with higher levels of engagement compared with traditional methods. Despite challenges related to technical infrastructure, lecturer readiness, and students’ digital competencies, the findings support the pedagogical relevance of immersive 3D technologies in medical education. Overall, the findings suggest that students perceive zSpace technology as supporting anatomical understanding and enhancing engagement within the studied context. Full article

This paper proposes and validates a hybrid FMEA-AHP framework designed to quantify the systemic asymmetries inherent in the automotive green transition. By moving beyond traditional component-level failure analysis, this model introduces the Market Maturity Coefficient (k_m), a novel diagnostic tool that benchmarks transition readiness by comparing risk profiles across divergent geoeconomic landscapes. Utilizing France and Romania as proxies for developed and emerging markets, the research demonstrates a significant “RPN Divergence,” where infrastructure and grid risks in emerging contexts are effectively amplified by factors exceeding 8.0× compared to developed counterparts. The results indicate that while software and technological risks show convergence (k_m ≈ 1.3), systemic vulnerabilities regarding grid stability and digital maturity remain the primary barriers to a uniform European transition. This framework provides policymakers with a mathematically rigorous method to move beyond “one-size-fits-all” policies toward targeted resilience funding. Full article

The rapid development of artificial intelligence (AI) has intensified debates regarding its impact on the labor market, specifically concerning the potential for replacement versus the augmentation of human labor. While the existing literature highlights both the opportunities and risks associated with AI, research conducted by faculty in academic settings focuses predominantly on academic integrity, paying limited attention to AI readiness and/or anxiety related to labor market entry. This study aims to compare the perceptions of students and faculty in Romania regarding the impact of AI on employment, exploring the role of personal and organizational readiness in shaping these attitudes. The research is based on an empirical approach utilizing a questionnaire applied to a sample of 271 respondents, consisting of 197 students and 74 faculty members. Data analysis included descriptive and inferential methods, such as Chi-square tests and binary logistic regression, and was theoretically grounded in the Unified Theory of Acceptance and Use of Technology (UTAUT) and Social Cognitive Theory (SCT). The results indicate significant differences between students and faculty regarding general attitudes toward AI, with students manifesting higher levels of concern regarding job replacement. However, both groups converge in their functional definition of AI as a major factor in labor transformation, suggesting an evaluative rather than a cognitive difference. Multivariate analyses show that personal readiness and the perception of organizational readiness are the primary predictors of a positive attitude toward AI, while demographic variables lose statistical significance when these dimensions are controlled. This study contributes to the literature by highlighting that AI-related anxiety is not inherently determined by demographic characteristics but represents a malleable state shaped by individual competencies and institutional conditions. The findings underscore the strategic role of universities in reducing perceptions of replacement and facilitating the transition to an AI-augmented labor market through training policies, adequate infrastructure, and transparent institutional communication. Full article

This article evaluates how artificial intelligence (AI) and educational technology (EdTech) support inclusive learning in Russia and Kazakhstan, two Eurasian countries that share post-Soviet educational legacies but differ in their levels of digital infrastructure and teacher preparedness. Using an asymmetric mixed-methods design, the study draws on a primary survey of N = 2570 educators and staff in four Russian cities (Moscow, Saint Petersburg, Yekaterinburg, Novosibirsk; October–December 2024; response rate 59.8%) and secondary policy/indicator analysis for Kazakhstan. Russia exhibits higher broadband access, AI/EdTech platform adoption, and teacher digital skill levels compared with Kazakhstan. Structural equation modeling (SEM; SmartPLS 4.1) tested four latent constructs—learning environment (LE), general digital competencies (HCg), specialized AI skills (HCs), and inclusion (I)—with satisfactory validity (AVE > 0.5; HTMT ≤ 0.85). A three-stage Measurement Invariance of Composite Models (MICOM) procedure confirmed configural, compositional, and full mean/variance invariance across Russian city subgroups, enabling pooled path analysis. Kazakhstan indicators from secondary sources are discussed as a descriptive benchmark. Semi-structured interviews with 24 stakeholders (12 Russia, 12 Kazakhstan; March 2025; analyzed with NVivo 14) revealed four themes: policy coherence, teacher readiness, infrastructure access, and ethical AI governance. Key SEM paths were LE → HCg (β = 0.278), HCg → HCs (β = 0.652), and HCs → I (β = 0.188), all p < 0.001. A formal mediation analysis confirmed a significant indirect effect across the full LE → HCg → HCs → I chain. The findings indicate that infrastructure is necessary but insufficient: the key to inclusion lies in sustained development of both basic and specialized digital skills, supported by coherent policies and continuous professional development. China and India are discussed as secondary international benchmarks drawn from published reports, not as sites of primary data collection. Full article

Although smart-city benchmarking has produced many indices and rankings, cities still lack a practical way to assess whether successful initiatives can be transferred across institutional contexts and converted into implementable urban roadmaps. In this study, we aimed to develop and empirically test a paired donor–recipient “problem–solution” methodology that bridges comparative city analysis with implementation readiness gap assessment, addressing the persistent disconnect between smart-city benchmarking and actionable transfer guidance. The smart-city ecosystem was decomposed into eight functional dimensions covering digital foundations, service platforms, finance and procurement, innovation capacity, governance, legal adaptability, and citizen participation. The method was applied to the Ottawa-Astana pair using a systematic desk-based analysis of publicly available strategic documents, legislation and policy frameworks, and implementation materials (e.g., roadmaps, program guidelines, departmental plans, and monitoring outputs). Data were analyzed using a structured gap analysis algorithm employing a three-level qualitative compliance scale (Full Compliance, Partial Compliance, and Non-compliance) to assess recipient city status against donor benchmarks across all eight functional dimensions. The results reveal Astana’s partial compliance with the Ottawa benchmark, with moderate readiness and pronounced “hard-soft” asymmetry; that is, greater progress in regard to infrastructure and platforms, but persistent gaps in adaptive regulation, experimentation-friendly legal instruments, and participatory governance. These findings suggest that progressing toward a Smart City 2.0 model requires prioritizing regulatory sandboxes, adaptive procurement pathways for pilots, and scalable civic-tech mechanisms alongside continued investment in talent and innovation ecosystems—understood here as interconnected networks of universities, technology parks, civic-tech communities, and incubation infrastructure that collectively sustain capacity for technology absorption and local adaptation. The proposed paired framework is replicable and supports phased, actionable transfer roadmaps for policymakers. Full article