Search Results (727)

As a frontier direction of engineering informatics, Digital Twin technology offers new technical possibilities for the whole-lifecycle governance of urban infrastructure. However, existing research mostly treats Digital Twin as a pure engineering system optimization tool, neglecting its governance attributes as a “Socio-technical System.” Based on the systematic literature review method, this paper retrieves relevant literature from databases such as Web of Science, Scopus, and CNKI between 2010 and 2025; systematically reviews the application evolution of Digital Twin technology in urban infrastructure across the planning, construction, operation and maintenance, and decommissioning stages from the three dimensions of technological embedding, organizational change, and institutional reconstruction; reveals the transmission mechanism through which technological capabilities are transformed into governance effectiveness via organizational mediation; identifies key existing research limitations in current research regarding data governance, cross-sector collaboration, and institutional adaptation; and proposes an integrative three-dimensional “Technological Embedding—Organizational Change—Institutional Reconstruction” analytical framework. This study finds that the core challenge of Digital Twin governance lies in coordinating the structural tension between technological centralization and governance decentralization; future research needs to transcend technological determinism, situate Digital Twin within a broader socio-technical system context, and achieve the substantive transformation from “technology empowerment” to “governance effectiveness.” Full article

Nowadays, speakers are essential components for message delivery in indoor environments, including lectures, public addresses, and emergency announcements. Their physical placement should ensure adequate direct-sound audibility across occupant service areas while maintaining installation feasibility. A digital twin is a technology that allows an infrastructure layout to be designed and evaluated virtually on a computer before physical installation by reconstructing an indoor environment as a 3D model. In previous studies, we have proposed a method to reconstruct a 3D indoor model of an indoor environment from its ${360}^{\circ }$ panoramic images using 3D Gaussian Splatting (3DGS) and a 3D point cloud, and applied it to surveillance camera placement. In this paper, we propose a digital twin-based speaker placement planning tool for indoor environments by generalizing the previous method to direct-sound acoustic simulation. This tool consists of four stages: (1) reconstructing a 3D indoor model from ${360}^{\circ }$ panoramic images and extracting floor and desk receiver surfaces, (2) assigning the initial speaker budget based on the reconstructed floor area, (3) determining speaker mounting positions on valid ceiling regions using K-means spatial clustering under obstacle and boundary constraints, and (4) simulating broadband direct-sound sound pressure level (SPL) across floor and desk receiver surfaces. For evaluation, the proposed tool was deployed across three real-world indoor scenarios: a basketball hall ($32.15 \mathrm{m}\times 21.99 \mathrm{m}$), a furnished office ($7.17 \mathrm{m}\times 6.17 \mathrm{m}$), and a non-convex L-shaped office ($23.00 {\mathrm{m}}^2$). The experimental results showed that in each scenario, the generated layout achieved complete direct-sound audibility compliance across all sampled physical test locations (≥60 dB floor/≥65 dB desk) with zero detected hotspots exceeding $85 \mathrm{dB}$, maintaining SPL values between $66.91 \mathrm{dB}$ and $77.88 \mathrm{dB}$. An on-site physical measurement campaign confirmed that the generated layouts satisfy target audibility thresholds under real room conditions, with mean absolute errors between $1.78 \mathrm{dB}$ and $2.41 \mathrm{dB}$. These results confirm the practical utility of our approach as an initial geometry-driven planning tool for indoor audio infrastructure deployment. Full article

The convergence of additive manufacturing (AM) and artificial intelligence (AI) is poised to redefine the landscape of modern production; however, the literature remains fragmented across isolated applications, lacking a unified perspective on the engineering impact and practical deployment of these technologies. This review provides a comprehensive and critical synthesis of the state of the art in AI-enhanced AM, systematically covering supervised, unsupervised, and reinforcement learning paradigms, alongside deep-learning-based computer vision, natural language processing, and robotics. In contrast to prior works that focus on singular aspects, this paper consolidates progress across four core engineering domains: (i) lightweight and manufacturable design, (ii) real-time in situ defect detection and process analysis, (iii) energy-efficient process optimization, and (iv) cost-effective build-time estimation with intelligent support minimization. Beyond cataloging these advances, this review identifies key quantitative benchmarks and recurring technical challenges, including data scarcity, poor model generalizability, and the critical gap between offline prediction and real-time closed-loop control. To transcend these isolated successes and enable industrial adoption, we propose a novel, unified closed-loop AI-AM framework that tightly integrates generative design, process planning, in situ production monitoring, and continuous model updating into a cohesive digital thread. Furthermore, a domain-stratified SWOT analysis is compiled, offering a strategic evaluation of strengths, weaknesses, opportunities, and threats across the four application pillars. By bridging the gap between laboratory prototypes and production-ready autonomous systems, this review serves as a definitive reference for researchers and practitioners aiming to navigate, deploy, and advance the rapidly evolving field of AI in additive manufacturing. Full article

The incorporation of Digital Twins (DT) into the healthcare industry marks a revolution in terms of adopting a more proactive and personalized approach towards patient care. The increasing complexity of technological tools employed within the healthcare environment leads to assessing the potential impacts of these digital models in collaboration with AI and IoT for increased efficiency and improved results. In this context, this study offers a systematic review of existing research regarding DTs in the field of healthcare, with specific consideration of hospital applications. An extensive literature search was performed within the Scopus database for peer-reviewed publications during the period from 2021 to 2026. Following a demanding screening process, 70 relevant articles were found that fulfilled the selection criteria. The review shows an emerging trend towards the application of AI-based Digital Twins in the real-time monitoring, predictive maintenance of medical devices, and planning surgeries. The paper analyses several key characteristics of healthcare DTs, including their design and architecture, and the benefits they generate. It also presents the challenges related to data integration and ethics surrounding virtual health models and recommendations for future research. In conclusion, this review demonstrates the revolutionary role of AI- and IoT-enabled Digital Twins in the transformation of hospitals’ infrastructures. This paper summarizes the latest developments and gaps in this field and offers a starting point for further research in this area. Full article

Decisions made during the pre-construction phase play a decisive role in the success of healthcare building projects. Although pre-construction critical success factors (CSFs) have received considerable research attention, few studies have transformed CSF-based expert ratings into a quantitative and interpretable performance index. This paper develops and applies a two-stage Mamdani Fuzzy Inference System (FIS) to assess pre-construction performance in healthcare building projects. Data were collected using an online survey completed by construction professionals experienced in healthcare projects. After excluding incomplete responses, careless responses, and multivariate outliers, 201 valid responses were retained. A total of 54 CSFs grouped into eight pre-construction domains were assessed on a five-point Likert scale. The Relative Importance Index (RII) values were calculated and utilized as fuzzy rule weights. In Stage A, CSF-level ratings were converted into eight group-level performance scores, and in Stage B, these scores were integrated to form a single Pre-Construction Healthcare Project Performance Index (PCHPPI). The model was developed in MATLAB R2025b and used five triangular membership functions. The results showed that the scores at group level varied from 63.9% for Technology and Digital Integration to 66.7% for Procurement Planning. The overall PCHPPI was 62.7%, classified as High. Sensitivity analysis was performed using alternate weightings, membership function settings, defuzzification techniques and ±5% input variations. The PCHPPI was classified as High in all examined situations, indicating that it is relatively robust to modest variations in model assumptions and input conditions. This indicates that healthcare pre-construction performance is generally satisfactory but remains closer to the lower boundary of the High classification, suggesting room for improvement. The PCHPPI and Performance Octagon provide a structured basis for performance assessment, diagnosis, and potential benchmarking across pre-construction domains, providing a practical assessment instrument for healthcare construction organizations and project teams. The proposed framework provides a structured decision-support approach for project readiness evaluation during the early planning of healthcare building projects. Full article

Digital twins (DTs) based on agent-based artificial intelligence (Agentic AI) provide a transformative framework for streamlining nondestructive testing (NDT) of 3D-printed rehabilitation equipment. This study applies a conceptual research methodology based on the integration and analysis of recent advances in Agentic AI, digital twin architectures, additive manufacturing, NDT technologies, and intelligent rehabilitation systems to establish a framework for autonomous quality monitoring and lifecycle management of 3D-printed medical devices. By creating intelligent virtual replicas of physical devices, these systems enable continuous monitoring of structural integrity, functional performance, and degradation mechanisms throughout the product lifecycle. Unlike conventional AI-based DTs, Agentic AI-driven DTs can autonomously perceive, reason, plan, and execute corrective actions based on real-time sensor data, NDT results, manufacturing information, and historical knowledge. The main conclusion of this work is that Agentic AI-enhanced DTs have the potential to transform NDT from a passive inspection approach into an intelligent, predictive, and autonomous decision-support system for rehabilitation equipment. Advanced machine learning and autonomous decision-making algorithms enable the identification of early signs of material degradation, manufacturing defects, fatigue accumulation, and performance anomalies, supporting predictive maintenance and proactive quality assurance. Integrating Agentic AI DTs with additive manufacturing processes enables real-time optimization of printing parameters, adaptive process control, and continuous refinement of inspection strategies without production interruption or destructive sampling, thereby supporting Industry 4.0 and smart manufacturing principles. The main innovation of this research lies in proposing an autonomous closed-loop framework that combines Agentic AI, DTs, additive manufacturing, and NDT into a unified system capable of continuous learning, reasoning, and operational optimization. Compared with existing studies that mainly focus on AI-assisted defect detection or static digital twin models, this approach introduces autonomous agents capable of coordinating sensing, simulation, diagnosis, prediction, and corrective actions across the entire lifecycle of 3D-printed rehabilitation devices. The proposed concept extends current digital twin applications by incorporating virtual stress testing, autonomous simulation, patient-specific customization, and adaptive device management, reducing dependence on physical prototypes, minimizing material waste, and accelerating design validation. By combining autonomous reasoning with predictive analytics, Agentic AI-based DTs represent a next-generation solution for intelligent, adaptive, and sustainable nondestructive testing, advancing both additive manufacturing technologies and personalized rehabilitation engineering. Full article

Community Mobile Health Vehicles (CMHVs) represent an innovative healthcare delivery model that integrates medical services into residents’ daily community life. By extending healthcare coverage to underserved populations, CMHVs can improve access to health management services while enhancing the efficiency of healthcare resource utilization. However, existing studies have primarily focused on health outcomes or the adoption of digital health technologies, with limited attention paid to users’ willingness to utilize CMHVs and their relationship with sustainability dimensions, including social equity, economic efficiency, and environmental responsibility. Drawing on survey data collected from community residents in China, this study employs Exploratory Factor Analysis (EFA) to identify the key determinants influencing users’ willingness to use CMHVs. Furthermore, the Entropy Weight Method (EWM) is applied to prioritize both the identified factors and their corresponding design strategies according to their relative importance. The results reveal five principal determinants: Cognitive Ease, System Adaptability, Institutional Trustworthiness, Technical Reliability, and Environmental Compliance. These factors and their associated design strategies exhibit varying levels of importance in promoting user engagement, optimizing resource allocation, and facilitating community integration. Based on the findings, this study proposes a set of sustainability-oriented design prioritization strategies for CMHV service systems. The proposed framework provides both theoretical and empirical insights for urban healthcare service planning and supports the coordinated achievement of economic, social, and environmental sustainability goals. The study further offers evidence-based guidance for the implementation, continuous improvement, and long-term sustainability of CMHVs within urban healthcare and mobile health service systems. Full article

In the field of architectural heritage, industrial built heritage plays an important role. Traditional research methods such as literature studies and field surveys have gradually evolved into digital-twin and machine-learning-based approaches. However, the study of industrial heritage remains a challenge. This study employs the PRISMA framework to conduct a systematic review of the existing literature, aiming to elucidate the impact of GIS on industrial heritage sites and its potential limitations. This study conducts a systematic review of GIS-based research on industrial heritage, focusing on studies published between 2000 and 2025. Among the 2166 records initially retrieved from Scopus and the 1771 records initially retrieved from Web of Science, 77 papers were ultimately retained after screening and deduplication. A total of 32 studies were selected based on thematic screening criteria, all of which focused on analysing the spatial characteristics of industrial heritage sites using GIS technology. Among these, 12 core studies were further selected for in-depth qualitative and methodological analysis. These documents form the foundation for exploring research hotspots, methodological approaches, and technological evolution. This review contributes a five-dimensional analytical framework that systematically classifies GIS-based industrial heritage research and identifies eight structural research gaps. The findings indicate that GIS applications remain predominantly focused on spatial mapping and distribution analysis, while their integration with ecological assessment, social and participatory approaches, archaeology, and multi-source technologies remains limited. This review provides a roadmap for integrating advanced spatial analysis tools into future industrial heritage research and planning. Full article

Extreme weather events increasingly affect the safety, durability, and operational performance of road infrastructure, creating the need for sustainable thermal management solutions. Among the available technologies, low-enthalpy geothermal systems offer significant advantages by providing continuous heating and cooling capabilities with reduced environmental impact compared to conventional maintenance practices. This study presents the methodology developed within the GEO-ROAD project to assess shallow geothermal resources across Spain and support the future deployment of geothermal road systems. The proposed framework integrates geological, thermal, and satellite-derived geophysical information through a unified GIS-based workflow, combining multivariate statistical analysis, map algebra, and automated geospatial processing to generate a regional geothermal potential model. In addition to conventional geological characterization, the methodology incorporates magnetic and gravity data from satellite missions, airborne surveys, and ground-based observations to improve the spatial representation of subsurface conditions. The resulting geothermal potential assessment constitutes a key component of the GEO-ROAD digital platform, where it will be combined with climatic risk maps and road infrastructure information to identify the most suitable locations for geothermal applications. By linking geothermal resource assessment with infrastructure-oriented decision-making, the proposed methodology provides a scalable and transferable framework for supporting the planning of sustainable and climate-resilient road thermal management systems. Full article

Background/Objectives: The integration of artificial intelligence (AI) and digital technologies into dental practice is reshaping clinical workflows, administrative processes, and, increasingly, the patient experience and the doctor–patient relationship. While prior research has documented the attitudes of clinicians and practice managers, the perspective of the patient remains comparatively underexplored. This study examined how dental patients perceive AI integration and digital tools across the dental care pathway, together with the associated implications for data security, cost, and the human dimension of care. Methods: A cross-sectional, questionnaire-based study was conducted among 200 dental patients in Bucharest, Romania, and the surrounding region. The instrument assessed perceived difficulty and availability regarding digital technology, current use of digital tools, demographic and educational characteristics (age, gender, practice environment, educational level), and two attitudinal dimensions, namely digital prudence and concern for technological sustainability, across five subdomains of the dental care pathway: scheduling, diagnosis, treatment planning, feedback, and follow-up (dispensarization). Responses were analyzed using non-parametric tests and exploratory principal component analysis with internal-consistency validation. Results: Patients expressed moderate-to-high interest in AI support during the diagnostic (median = 3.3, IQR = 2.7–3.9) and feedback (median = 3.11, IQR = 2.78–3.67) stages and the lowest interest in scheduling (median = 2.7, IQR = 2.0–3.3). A marked level of digital prudence was observed (median = 3.24, IQR = 2.82–3.61), reflecting concerns about data security, automation, and a possible weakening of the clinician–patient bond. Younger and academically educated patients reported lower perceived difficulty, higher availability, and greater current use of digital tools (all p ≤ 0.001); counterintuitively, the same patients scored significantly higher on digital prudence (Spearman’s ρ = −0.260, p < 0.001). Greater familiarity with digital tools was therefore accompanied by a more critical awareness of their informational risks rather than by uncritical acceptance. Conclusions: Dental patients approach AI through a dual lens of openness and informed caution, welcoming efficiency gains in the clinical and continuity-of-care stages while voicing measured concerns about data security, affordability, and the preservation of human contact. To interpret this profile, we propose two conceptual contributions: a mapping of patient needs onto Maslow’s hierarchy in the context of AI-mediated care and the Informational VUCA framework, which characterizes the volatility, uncertainty, complexity, and ambiguity that patients face when navigating AI-generated information. The findings point to a clear practical agenda of transparent communication, robust data governance, and education strategies adapted to patients’ educational and demographic profiles, so that AI-enhanced workflows strengthen rather than erode the doctor–patient relationship. Full article

Effective welfare monitoring in laying hens is increasingly challenged by growing flock sizes, declining farm numbers, and the practical limitations of assessor-based protocols under commercial conditions. Precision Livestock Farming (PLF) technologies offer opportunities to support large-scale welfare assessment, yet their application in laying hens remains predominantly limited to small-scale or prototype systems. This study addresses three complementary objectives aimed at informing future computer-vision-based PLF research and development in commercially housed laying hens. First, a systematic literature review was conducted to identify welfare-related categories monitored in laying hens between 2005 and 2025, the methods used to assess them, and the extent to which automated monitoring approaches have been applied. Second, outcomes of a TransRegional Expert Panel (TREP) within the OMELETTE project were synthesised to rank priority welfare challenges, evaluate the feasibility of different monitoring approaches, and compare expert perspectives with trends identified in the literature. Third, two pan-tilt-zoom (PTZ) camera setups implemented in semi-commercial aviary systems were described as proof-of-concept examples illustrating how multiple welfare challenges can be monitored using a single, multipurpose camera system. The literature review revealed a pronounced imbalance in monitoring frequency, with feather pecking dominating the literature while several other welfare challenges, including piling, disturbed sleep, and toe-pecking, remain comparatively underrepresented. TREP outcomes confirmed feather pecking as the highest-priority welfare challenge but also highlighted the importance of integrated monitoring approaches that combine time-intensive assessments, shorter checklists, and automated systems rather than relying on single indicators. Experts further considered the use of digital devices during routine barn inspections to be practically feasible. The PTZ proof-of-concept demonstrates how priority welfare challenges identified through both literature and expert input can be operationalised through automated, scheduled, and location-specific monitoring under commercial conditions. Together, these findings highlight the need for future PLF research to move beyond isolated measurements and small-scale trials towards integrated, cost-effective, and farm-specific welfare-monitoring systems that support adaptive, data-informed management strategies—for example, within a Plan–Do–Check–Act framework—and enable the development of digital standard operating procedures that generate actionable insights under real-world commercial constraints. Full article

Digital behavior change tools increasingly integrate constructs from multiple theoretical traditions, yet there is limited cross-domain understanding of how these frameworks are combined and which outcomes they are used to explain. This scoping review therefore aimed to map integrated theoretical frameworks used in digital IT-based behavior change tools from 1999 to 2025, focusing on theory combinations, outcome types, intervention contexts, and research gaps. Following PRISMA-ScR and a registered protocol (PROSPERO CRD42022285741), searches across six databases identified 62 eligible studies. Twenty-nine theories and models were identified, with Self-Determination Theory (35%) and the Theory of Planned Behavior (29%) being the most prevalent. Behavior-change-oriented frameworks were more commonly associated with target behavioral outcomes, whereas technology-adoption-oriented frameworks primarily explained intention, acceptance, continuance, and technology use. Health and fitness interventions dominated the evidence base (44%), followed by online learning (23%) and mobile commerce (11%). Long-term follow-up and explicit theory-to-behavior-change-technique mapping remained limited. Overall, the review provides a 25-year cross-domain synthesis of theoretical integration in digital behavior change tools and highlights the need for clearer theory–intervention alignment, longer-term evaluation, and broader application across underrepresented digital contexts. Full article

Digital transformation in higher education institutions requires approaches that recognize the diversity of organizational contexts and the varying priorities expressed by academic units. This study analyzes digital transformation objectives identified in the institutional development plans of six faculties and schools at the Universidad Nacional Autónoma de México using a document-based frequency prioritization approach. The study examines how institutional plans articulate digital transformation objectives in relation to organizational characteristics, management structures, and available resources, adopting a systems perspective to account for the interactions among these organizational conditions. A key contribution of this research is the use of institutional planning documents as an analytical basis for identifying seven core dimensions that characterize digital transformation initiatives in the university-analyzed context, providing a framework for understanding how academic units define and organize their transformation priorities. The proposed frequency-based prioritization approach operationalizes this comparison by calculating the relative weight of each dimension based on the number of technological objectives classified within it. The findings show that digital transformation priorities vary according to institutional autonomy, management arrangements, disciplinary profiles, existing capabilities, and available resources. In large and diverse universities characterized by decentralization and complex organizational structures, digital transformation does not follow a single pathway; instead, it develops through context-dependent strategies shaped by the specific conditions and needs of each academic unit. Full article

Blockchain technology (BCT) is often discussed as digital support for timber traceability in the context of the EU Deforestation-Free Products Regulation (EUDR), which requires operators and traders to demonstrate legality, deforestation-free production, geolocation at the forest parcel level, and verifiable supply chain documentation from 30 December 2026, with an extended timeline for micro, small, and medium-sized enterprises (SMEs) until 30 June 2027. This study assesses where BCT can realistically add value in timber supply chains under operational forestry conditions and identifies the necessary technical, organizational, and legal prerequisites. A combination of a targeted literature review and empirical input from experts in the forestry and timber industry, including guided expert web-conferencing interviews (n = 41), an online survey (n = 69 completed responses), and a transdisciplinary workshop (n = 18) was utilized to obtain a comprehensive overview of industry perspectives. Qualitative data from interviews and workshop sessions were analyzed using structured qualitative content analysis, while survey data were evaluated using descriptive statistics. The expected benefits are associated with the introduction of tamper-proof timber harvesting practices and cross-organizational verification mechanisms. To address the discrepancy between biological uncertainty and digital rigidity, the study proposes a dynamic allocation model adapted from the energy sector that distinguishes between fixed and variable wood capacities to automate logistical planning via smart contracts. However, respondents emphasize that practical obstacles, such as limited digital maturity in forestry, fragmented data infrastructures across the supply chain, and unresolved issues of data sovereignty hinder the implementation of BCT. BCT alone is unable to resolve the problem of weak physical-digital identity continuity, a phenomenon widely known as the oracle problem; however, coupling the ledger with physical or biological anchors (e.g., photo-optical, automated inkjet marking identification) can re-establish this physical–digital continuity and thereby resolve the oracle problem. The results demonstrate that blockchain acts most plausibly as a supporting component within hybrid traceability architectures that prioritize event-based authentication, off-chain data processing where appropriate, and integration with existing certification systems. The study highlights the necessity of defining distinct organizational roles and responsibilities while integrating user-centric digital solutions tailored specifically to small and medium-sized enterprises (SMEs). Full article

Objectives: To synthesize current evidence regarding advances in periodontal diagnosis and therapy, with emphasis on molecular biomarkers, omics technologies, microbiome profiling, digital imaging, and artificial intelligence-based analytical models that support the transition toward precision periodontology. Methods: This narrative review examines contemporary evidence on emerging molecular, microbiological, and digital technologies applied to periodontal diagnosis, prognostic assessment, and therapeutic planning. The review includes studies addressing salivary and gingival crevicular fluid biomarkers, microbiome characterization, omics approaches, cone-beam computed tomography, three-dimensional imaging, machine-learning algorithms, and personalized periodontal therapies. Relevant literature was identified through searches in major biomedical databases, including PubMed/MEDLINE, Scopus, and Web of Science, focusing on studies published on periodontal diagnostics, biomarkers, digital technologies, artificial intelligence, and precision medicine approaches in periodontology. Results: Peer-reviewed articles addressing innovative diagnostic and therapeutic approaches in periodontology were considered. Priority was given to studies evaluating clinical applicability, diagnostic performance, prognostic utility, and personalized treatment strategies integrating molecular and digital technologies. Conclusions: Emerging molecular and digital technologies are reshaping periodontal diagnosis and therapy by improving disease detection, risk prediction, and individualized treatment planning. Biomarkers, omics technologies, microbiome profiling, and artificial intelligence-assisted imaging may enhance diagnostic precision and clinical decision-making. These developments support the implementation of precision periodontology; however, challenges related to biomarker validation, algorithm standardization, cost, and accessibility remain barriers to routine clinical adoption. Further research is necessary to validate these approaches and facilitate their integration into periodontal practice. The integration of biomarkers, omics technologies, advanced imaging, and artificial intelligence may improve early periodontal diagnosis, prognostic assessment, and personalized treatment planning. These innovations support the transition toward precision periodontology and have the potential to enhance clinical decision-making, treatment outcomes, and long-term periodontal health in routine dental practice. Full article