Search Results (125)

Digital-twin-enabled human–machine collaboration (HMC) has increasingly been proposed as a system-level approach for connecting human operators, robots, sensors, artificial intelligence modules, and manufacturing resources. However, the literature varies substantially in what is called a digital twin, how physical and virtual models are coupled, whether models are updated from physical data, and how far systems have progressed beyond simulation or controlled laboratory demonstrations. This structured integrative review examines the conditions under which a digital twin can function as an integration layer for HMC in sustainable smart manufacturing, rather than assuming that such integration is already established industrial practice. The literature corpus was assembled through searches of the Web of Science Core Collection, Scopus, and IEEE Xplore, complemented by Google Scholar-based citation tracking and backward and forward citation tracing. The core search focused on studies published from 1 January 2020 to 5 August 2026, while earlier seminal studies were retained to support definitions and historical context. Studies were screened using explicit criteria for manufacturing relevance, physical–virtual coupling, state synchronization or model updating, feedback capability, and validation setting, and were critically coded by model type, integration mechanism, deployment maturity, and sustainability evidence. The review compares multimodal perception and human-state modeling, intention understanding and augmented interaction, task allocation and shared planning, digital-twin architectures, adaptive control and safety verification, and human–AI decision-making. The evidence indicates that digital twins are promising as coordination and verification layers, but many reported systems remain conceptual, simulation-based, or limited to controlled physical prototypes. Key barriers include model fidelity, online model updating, real-time synchronization, cross-platform interoperability, safety assurance, human-data governance, and the limited availability of directly measured sustainability outcomes. Future work should prioritize validated hybrid models, traceable model-update mechanisms, staged virtual-to-physical deployment, interoperable data contracts, and longitudinal evaluation of technical, human, economic, and environmental performance. Full article

This paper presents a quantitative evaluation of an automatic translation system between industrial robot programming languages based on a sequence-to-sequence (Seq2Seq) neural architecture using Long Short-Term Memory (LSTM) networks. The study addresses the interoperability problem between proprietary robot programming languages by proposing a data-driven approach capable of learning correspondences between structured code instructions. A parallel dataset of 28,000 aligned instruction pairs was constructed and preprocessed through tokenization and normalization to enable structured sequence learning. The model was trained under four configurations (50,100, 150 and 200 epochs) to analyze the impact of training duration on performance and generalization capability. The system was evaluated using multiple quantitative metrics, including accuracy, loss, BLEU, and Exact Match (EM), allowing assessment of both structural similarity and exact sequence correctness. Experimental results demonstrate that the 200-epoch configuration improves the performance across all metrics, achieving an accuracy of 0.9943, a BLEU score of 0.682, and an Exact Match of 0.970 on the test set. These results indicate that the model is capable of generating both structurally consistent and syntactically correct translations. The analysis shows that while BLEU captures structural similarity, EM provides a stricter evaluation of exact sequence correctness, which is critical in structured code translation tasks where minor variations may affect execution. The proposed approach demonstrates the feasibility of applying neural machine translation techniques to industrial robot programming, contributing to improved interoperability and reduced manual effort in multi-platform robotic environments. Full article

Background: Dental insurance claims data are vital for research in oral health, epidemiology, and policy. However, issues like data quality, coding standards, validity, interoperability, and analytical approaches hinder their use. This review outlines these challenges. Methods: Following PRISMA-ScR and Arksey-O’Malley, we searched Web of Science, Scopus, and PubMed through April 2026 for peer-reviewed studies on dental insurance data issues. Two reviewers screened and extracted data, identifying key challenges and implications. Results: Out of 563 records, 389 remained after deduplication; 45 studies met criteria. Data sources included Medicaid, Medicare, insurers, and national systems from various countries. Six main challenges emerged: (1) coding errors and lack of standardization; (2) data validity and quality concerns; (3) interoperability and linkage barriers; (4) fraud detection issues; (5) analytical limitations; (6) policy insights on disparities. Validation showed variable accuracy, with diagnosis codes more reliable than procedure codes. Conclusions: Challenges limit data use in research and policy. Standardized coding, validation, interoperability, transparency, and causal inference are essential for leveraging these data to improve oral health research and policies. Full article

Smart Factory programs increasingly connect shop-floor, quality, asset and planning data, but integrated data infrastructures do not necessarily align operational decisions. This paper reviews how smart manufacturing literature explains the transition from industrial data integration to closed-loop operations synchronization and value capture in high-throughput manufacturing contexts. Using the Systematic Search Flow method, 1949 records were screened and reduced to a final portfolio of 73 studies. The papers were coded by thematic cluster, dominant technology, research method, primary theme, value-stream coverage and operations-synchronization relevance. The coding shows that roadmaps, interoperability architectures, analytics applications and digital-twin models dominate the portfolio. Explicit operations-synchronization mechanisms are addressed in 16 of the 73 studies, mainly through planning-execution coupling and digital-twin-based decision support. Coverage across value streams is uneven, with stronger evidence for Strategy, Make and Plan than for Quality and Assets. Based on this evidence map, the paper proposes a Data-Driven Operations Synchronization Stack that links operational data capture, semantic and IT/OT interoperability, analytics-supported decision-making, closed-loop synchronization and operational or financial value capture. Full article

Digital Twins are increasingly viewed as key enablers of real-time decision-making in supply chain management and logistics, yet the literature remains fragmented across application domains, decision problems, methodological approaches, and technological implementations. This systematic review examines how Digital Twins support real-time decision-making, understood not as a fixed response time threshold but as the temporal alignment between data refresh, decision generation, and system evolution. Following the PRISMA 2020 framework, a Scopus search conducted on 16 March 2026 identified studies in which Digital Twins were a central component, incorporated dynamically updated data, supported real-time or near-real-time decision-making, and addressed supply chain management or logistics problems. A total of 57 peer-reviewed studies were retained and synthesized using descriptive analysis, cross-tabulation, and thematic coding across decision problems, application contexts, solution methods, enabling technologies, implementation challenges, and future research directions. The findings show that real-time Digital Twin applications are concentrated mainly in production scheduling and planning, followed by routing and dispatching, resource allocation, disruption management, and inventory management. Methodologically, most studies adopt hybrid approaches combining simulation, optimization, and/or machine learning, reflecting the complexity of real-time operational decision-making. Enabling technologies were grouped into six functional layers, with data acquisition technologies receiving the greatest attention, while higher-level integration and enterprise system layers remain less developed. Reported challenges cluster around data, methodological, and systemic issues. The review concludes that real-time Digital Twins are emerging as integrated decision environments, but further research is needed on computational efficiency, interoperability, validation, and application in underexplored logistics domains. Full article

The fragmentation of clinical information across health systems, community pharmacies, and specialty providers continues to undermine medication safety and emergency care, particularly when patients are unconscious or otherwise unable to communicate their history. The dominant response to this fragmentation has been the construction of a centralized data infrastructure—health information exchanges, prescription drug monitoring programs (PDMPs), and federated electronic health record (EHR) networks—that aggregates clinical information into institutional databases that are queryable by providers, insurers, regulators, and, in many jurisdictions, law enforcement. This article argues that the same care-coordination problems can be addressed through an architecturally different approach in which the patient, not the institution, holds the integrative artifact. The proposed design, here labeled the Guardian Card (a conceptual architecture, not a commercial product), pairs an HL7 Fast Healthcare Interoperability Resources (FHIR) clinical payload with the SMART Health Cards verifiable-credential framework and a dual-modality (QR code plus near-field communication) physical carrier. After describing the technical architecture, hardware options, and a five-phase deployment roadmap, the design is situated within the surveillance-critical scholarship that has documented PDMP function creep, third-party doctrine erosion, racial disparities in algorithmic prescribing oversight, and the surveillance-instrumentarian repackaging of nominally de-identified prescription data. The Guardian Card is offered as one operational implementation of a patient-controlled medication-record architecture, with community pharmacy and long-term post-acute care, where the Pharmacist eCare Plan integration is most feasible as a recommended first-deployment venue. Full article

3D Gaussian Splatting (3DGS) has recently emerged as an effective representation for immersive 3D scene rendering, providing high visual fidelity and real-time rendering efficiency. To support interoperable compression of trained 3DGS content, the Moving Picture Experts Group (MPEG) is exploring Gaussian Splat Coding (GSC), which mainly targets already trained 3DGS models following the INRIA reference format. The current video-based GSC anchor reorders 3DGS attributes into 2D attribute maps using Parallel Assignment Linear Sorting (PLAS) and compresses the resulting maps using High Efficiency Video Coding (HEVC). However, higher-order spherical harmonic coefficients (SH-AC) often remain irregular and exhibit low local spatial correlation even after PLAS reordering, limiting the coding efficiency of conventional video codecs. This paper proposes a VQ-HEVC hybrid compression framework that is structurally compatible with the video-based GSC anchor framework, in which SH-AC coefficients are represented by vector quantization (VQ) indices, while the remaining attributes are encoded using the same HEVC-based procedure as the GSC anchor. The proposed method adopts a two-stage VQ scheme that combines coarse VQ and product-quantization-based residual quantization, together with zero-masked residual VQ and flexible PQ grouping, to improve index-map coding efficiency across rate points. The generated VQ indices are packed into YUV400 index-map sequences and encoded using HEVC lossless coding, while the corresponding codebooks are transmitted as metadata. Experimental results on the Bartender and Cinema sequences of the MPEG GSC CTC demonstrate consistent rate–distortion improvements over the video-based GSC anchor across multiple objective quality metrics within the evaluated setting. In terms of RGB-PSNR, the proposed method achieves BD-rate reductions of 22.3% and 18.5% for the Bartender and Cinema datasets, respectively. These results suggest that, for the evaluated GSC CTC sequences, VQ-based SH-AC representation can effectively complement PLAS-based video coding while maintaining consistency with the existing GSC coding structure. Full article

This review traces the four-generation evolution of non-destructive evaluation (NDE 1.0–4.0) and audits where the field genuinely stands today. The central finding is that statistically qualified probability of detection (POD), as defined in MIL-HDBK-1823A and related frameworks, is not interchangeable with machine-learning metrics such as accuracy or F1-score; the two answer different questions and rest on different statistical foundations. Reported AI performance on curated datasets does not, by itself, predict field reliability because domain shift, sensor variability, and class imbalance change the inspection signal once a model leaves the lab. Six recurring barriers limit industrial uptake: scarce open benchmark datasets, domain shift, weak interoperability, explainability constraints, cybersecurity exposure, and the lack of broadly accepted code provisions for AI-derived accept/reject decisions. The oil and gas sector is used as a case study because it combines high inspection volume, severe operating environments, mature risk-based inspection practice, and strong regulatory conservatism. NDE 4.0 is technically credible; its wider acceptance in safety-critical industries will be earned through representative field validation, auditable model governance, standardised data structures, and qualification pathways—not through stronger laboratory accuracy claims. Full article

The building sector is a critical component of the European Union’s strategy to achieve climate neutrality, as it accounts for 30–40% of total energy consumption and significant greenhouse gas emissions. While sustainability certification systems like BREEAM, LEED, DGNB, and HQE have established frameworks for environmental assessment, their widespread adoption in the residential sector faces challenges related to complexity and technical barriers. This paper provides a state-of-the-art literature review on streamlining sustainability certification for residential buildings in the EU. It examines the transition from established private schemes to harmonised frameworks such as Level(s), alongside the integration of Building Information Modelling (BIM) and Life Cycle Assessment (LCA). The review identifies key obstacles, including data interoperability issues, the need for automated quantity extraction, and the lack of technical expertise among stakeholders. Findings suggest that streamlining requires advancing semantic data models and digital twins to enable real-time performance monitoring and automated compliance checking. Furthermore, the alignment of national building codes with the Energy Performance of Buildings Directive (EPBD) and the European Green Deal is essential for fostering a more cohesive certification landscape. The study concludes by outlining pathways for reducing the administrative and technical burden of certification to support the EU’s decarbonisation and renovation goals. Full article

Urban flexibility research is expanding across buildings, electric vehicles (EVs), distributed energy resources (DERs), storage, positive energy districts (PEDs), digital twins, and interoperability platforms. These strands are often reviewed separately, although urban distribution operators must manage their combined impacts on the same feeders. This paper presents a PRISMA 2020-aligned systematic review with evidence mapping and narrative synthesis of feeder-aware coordination in smart-city electricity systems. Searches of Scopus, Web of Science, IEEE Xplore, ScienceDirect, and citation chasing identified 312 records; 127 studies were included after screening and eligibility assessment, 101 entered the quantitative mapping sample, and 31 formed the deep-synthesis anchor core. Sparse contingency tables were analyzed with Monte-Carlo permutation chi-square tests and bootstrap confidence intervals for Cramér’s V, while ordinal variables were summarized with medians and interquartile ranges. Explicit feeder grounding was concentrated in grid-oriented and EV-oriented studies, whereas many AI/digital-twin and interoperability studies were less often validated against distribution-network operation. Economic and peak-flexibility indicators were reported far more often than interoperability, cybersecurity, or validation-maturity indicators in the anchor core. The synthesis also showed that deployment-oriented work depends on clearer treatment of standards, co-simulation workflows, regulatory instruments, and stakeholder roles. The evidence base is heterogeneous, English-only, and single-coded, so the quantitative results are descriptive rather than population-level. The review contributes a transparent three-layer corpus design (127 included/101 mapped/31 anchor), a domain-specific specialization of SGAM/IEEE 2030 for urban feeder orchestration, an operational digital-twin definition and validation ladder, a retrofittable benchmarking framework, and a practical roadmap for DSOs, municipalities, aggregators, EV operators, building managers, and ICT providers. Full article

Modular construction (MC) is frequently promoted as a path to circular economy (CE) outcomes in built environments, yet circular adoption and performance remain uneven. This study investigates how systemic barriers shape the implementation of circular strategies in MC. A systematic literature review combined with bibliometric mapping and systems-oriented synthesis was conducted using 124 Web of Science records published between 2011 and August 2025. Bibliographic coupling, co-citation, and keyword co-occurrence analyses were used to characterise the field’s intellectual structure, while 30 studies were selected for thematic coding and systems mapping. Ten recurrent barriers were identified and consolidated into six clusters: technical, financial, regulatory, stakeholder and organisational, quality assurance, and institutional and knowledge-based challenges. Their relative severity was assessed across four MC-relevant circular strategies: reuse, repurposing, design for disassembly, and multifunctionality. Systems mapping revealed three reinforcing feedback dynamics involving financial, stakeholder, and supply-chain pressures, knowledge and quality assurance constraints, and regulatory and design lock-in effects that stabilise conventional delivery and constrain circular implementation. Despite being underrepresented in the literature, multifunctionality emerges as a cross-cutting leverage point for enabling adaptable modular systems. The study synthesises five implementation pathways, including adaptable multifunctional design, interoperable interfaces, digital traceability, collaborative life-cycle integration, and policy alignment, and outlines systems-derived leverage points to guide future research and practice in circular modular construction. Full article

Scope-3 emissions from construction supply chains (CSCs) account for the majority of the construction sector’s greenhouse gas (GHG) footprint. However, procurement-driven decarbonization (PDD) remains constrained by persistent data quality (DQ) deficits, including boundary divergence, limited verification, incomplete information, and fragmented interoperability. This PRISMA-guided systematic literature review (SLR) synthesizes 68 studies to examine how governance mechanisms (GMs) and digital technologies (DTs) can be co-designed within procurement workflows to improve the reliability of carbon data. By integrating quantitative matrix-based analysis, qualitative thematic coding, and a governance–technology pairing logic, the review identifies a division of labor across DQ dimensions. Standard-based governance and boundary rules strengthen completeness, consistency, and interpretability. At the same time, DTs enhance accessibility and timeliness and provide targeted improvements in accuracy and logical coherence when embedded within structured schemas. Assurance emerges as the most reliable mechanism for accuracy, information-management standards for timeliness, and early stakeholder involvement for accessibility. These insights translate into procurement-oriented measures, including European Standard (EN)-aligned scope definitions; ISO 14083-aligned logistics accounting; Industry Foundation Classes (IFC)/Level of Information Need (LOIN)-based information requirements; selective assurance; uncertainty-aware disclosure; and integrated digital measurement, reporting, and verification (MRV) systems combining Environmental Product Declaration (EPD) platforms, Artificial Intelligence (AI) validation, and blockchain. Collectively, these measures enable comparable, verifiable data and support scalable decarbonization. Full article

This paper proposes a CBDC design trilemma, the claim that central banks cannot simultaneously maximize privacy, financial stability, and regulatory compliance when designing retail central bank digital currencies and finds the existing literature consistent with this proposition. Through a systematic review of 140 peer-reviewed articles (Web of Science SCIE/SSCI indexes, 2014–2026, supplemented by Scopus and SSRN), evidence is synthesized across four thematic dimensions: design frameworks and architecture, financial stability and banking risk, privacy and security trade-offs, and user adoption and institutional quality. Cross-tabulation of coded data supports all three pairwise tensions: privacy-enhancing designs weaken AML/CFT enforcement, anonymous holdings amplify bank-run risk, and stringent prudential safeguards constrain transaction monitoring. The literature converges on two-tier, hybrid architectures with tiered privacy as the dominant compromise a “zone of feasible design”, that sacrifices full optimality on each vertex. Nine research gaps are identified, most critically the scarcity of empirical evidence from live deployments, the neglect of wholesale CBDC, and insufficient analysis of cross-border interoperability. The framework offers policymakers a structured lens for evaluating retail CBDC design trade-offs and researchers a testable proposition for future empirical work. Full article

While integrating blockchain technology into Business Process Management (BPM) has gained attention, existing compilation-based approaches suffer from high redeployment costs and isolated network structures. This study proposes an FSM-based workflow interpreter engine utilizing the Inter-Blockchain Communication (IBC) protocol within the Cosmos ecosystem to overcome these limitations. The proposed system adopts an interpreter architecture that treats business logic as lightweight JSON specifications instead of hard-coding it into smart contracts. This separation allows for process updates through data modification rather than contract redeployment, significantly increasing operational flexibility. Furthermore, custom IBC packet structures were designed to enable seamless cross-chain process synchronization between independent application-specific blockchains. Experimental results demonstrate that the interpreter approach reduces process update costs by over 90% compared to conventional compilation methods. Additionally, gas consumption exhibited a linear growth pattern relative to task count and gateway complexity, ensuring cost predictability for large-scale business scenarios. Interoperability validation using a standard Procurement Order (PO) process showed successful cross-chain state transitions with a latency of approximately 1.45 s. This research provides a practical solution for building trust-based decentralized collaboration ecosystems by simultaneously achieving operational efficiency and interoperability in blockchain BPM. Full article

FHIR was designed for transactional interoperability but is less well suited to querying and exploratory analysis because its resource-centric structure distributes meaning across deeply nested resources. To address this limitation, we transformed MIMIC-IV Demo FHIR data into an OWL-compliant knowledge graph by flattening nested elements, normalizing repeating arrays, resolving inter-resource references, and promoting frequently queried attributes to direct properties. We also aligned diagnosis and procedure codes to ICD-9-CM and ICD-10-CM terminologies and developed a schema-grounded NL2SPARQL interface for natural-language querying. Structural validation was performed with SHACL and OWL reasoning. Across a curated evaluation set, NL2SPARQL achieved a mean accuracy exceeding 95% relative to expert-authored queries. These results suggest that ontologizing FHIR can improve analytic accessibility while preserving clinically meaningful assertions. Full article