Search Results (3,358)

Traditional Post-Occupancy Evaluation (POE) is static and incompatible with dynamic systems like Digital Twins, creating a digital gap in managing health-oriented urban environments, especially in Urban Underground Spaces (UUS). This paper bridges this gap with a deep learning framework that automates the continuous prediction of human physiological arousal. We created a novel multimodal dataset from in situ experiments, synchronizing first-person video, environmental data, and Galvanic Skin Response (GSR) as a real-time physiological arousal proxy. Our dual-branch spatial–temporal model fuses these data streams to predict GSR with high accuracy (Pearson’s r = 0.72), effectively mapping objective environmental inputs to continuous human physiological dynamics. This framework provides an automated, human-centric analysis engine for urban planning, design validation, and real-time building management. It establishes a foundational ‘human dynamics layer’ for urban Digital Twins, evolving them into predictive tools for simulating human-environment interactions and embedding physiological perception into intelligent urban systems. Full article

Complex products and production processes are intertwined and demand expressive, lifecycle-wide digital representations. The Asset Administration Shell emerged as a standard for Digital Twins (DTs), structuring heterogeneous data across cloud-based Industrial Internet of Things (IIoT) infrastructures. However, today’s deployments predominantly realize passive or reactive DTs, while intelligent behavior remains underexploited. This paper addresses this gap, proposing an end-to-end architecture operationalizing the DT Reference Model through the integration of machine-interpretable granulated industrial skills, which are semantically accumulated into a knowledge graph enabling discovery and reasoning, while a multi-agent system provides autonomous, utility-based negotiation via machine-to-machine interactions within a federated marketplace. The approach is applied in a real smart manufacturing demonstrator, combining order processes, production orchestration, and lifecycle documentation into a unified execution pipeline spanning IIoT-connected shopfloor assets and cloud-based services. Quantitative experiments evaluating negotiation latency, renegotiation robustness, and utility variation demonstrate stable, predictable behavior even under concurrent demand and failure scenarios. The architecture lays a foundation for interoperable, sovereign collaboration across value chains to realize shared production. The results underline the effectiveness of the tightly coupled enabler technologies realizing proactive, reconfigurable, and semantically enriched intelligent DTs. Full article

Design coordination is a critical process for avoiding spatial conflicts and ensuring design alignment in large-scale construction projects. While Building Information Modelling (BIM) tools have improved coordination through 3D model integration and clash detection, inefficiencies persist due to fragmented workflows, frequent tool switching, and challenges with issue documentation. Cloud-based BIM collaboration tools offer a promising alternative by enabling real-time model sharing, centralized issue tracking, and enhanced stakeholder communication. However, empirical evidence on their practical implementation and effects on coordination processes remains limited. Unlike prior cloud-BIM reviews that focus on technical capabilities or adoption barriers in isolation, this study provides an empirically grounded framework that links specific tool features to observable workflow changes and their downstream impacts on coordination outcomes. This study investigates the impact of cloud-based BIM collaboration tools on the design coordination process, with a focus on issue identification, resolution, and documentation. A framework was developed using a mixed-methods approach comprising action research, an ethnographic case study, and comparative analysis of three large infrastructure projects to categorize workflow changes resulting from tool adoption. The findings indicate that cloud-based BIM tools streamline coordination by reducing manual transitions, automating documentation, and improving information accessibility during meetings. Nevertheless, their effectiveness is constrained by organizational structures and contract limitations. This study provides a validated process-change framework and practical insights for engineering managers seeking to align digital collaboration tools with project delivery strategies, contributing to both theory and practice in BIM-based coordination and digital transformation in the AEC industry. Full article

Atypical joint spaces, such as those encountered in complex segmental bone loss and large structural defects, remain challenging to manage with conventional implants within divisions across orthopedics, including arthroplasty, tumor reconstruction, trauma, and spine. Additive manufacturing advances have made patient-specific implants a possibility, and this promising solution has enabled the creation of implants with customized geometry and controlled surface porosity to enhance osseointegration, reduce rejection rates, optimize biomechanics, and promote longevity. Despite its potential, patient-specific implants are still eclipsed in use by conventional, “off-the-shelf” implants due to their lower cost, documented long-term durability, insurance coverage, and the strength of available clinical evidence supporting their use. This narrative review summarizes current materials and manufacturing approaches for additively manufactured metal porous implants, including imaging and design workflows, lattice and pore architecture, and how the printing process influences implant stiffness, fatigue strength, surface roughness, and porosity. We also discuss the experimental and preclinical data on mechanical performance, fatigue resistance, and osseointegration for new developments in the field. Emerging trends such as material innovation, streamlined digital planning-to-implant workflows, 4D printing and other advanced additive manufacturing concepts, and cost-reduction efforts are examined in the context of clinical practicality. In this review, the integration of engineering principles with early clinical outcomes will provide orthopedic surgeons with a realistic understanding of the benefits and limitations of the future utilization of additive manufacturing in clinical practice. Full article

As a transformative force, the digital economy serves as a critical engine for driving high-quality economic development and fostering New Quality Productive Forces (NQPF)—characterized by high technology, high efficiency, and high quality. Viewing the establishment of China’s National Digital Economy Innovation and Development Pilot Zones as a quasi-natural experiment in economic system management, this study employs a Double Machine Learning (DML) framework to evaluate its systemic impact on A-share listed companies from 2015 to 2023. Unlike traditional linear models, the DML approach flexibly controls for high-dimensional confounding variables and functional form misspecification, thereby ensuring highly rigorous causal inference. The empirical results demonstrate that these pilot zones create an optimized “digital environment” that significantly enhances corporate NQPF, a conclusion that remains highly robust across a comprehensive battery of robustness and endogeneity tests. Mechanism analysis reveals three systemic transmission pathways through which the policy operates: optimizing factor allocation, deepening digital technology empowerment, and promoting green innovation and sustainability. Furthermore, heterogeneity analyses indicate that the policy’s efficacy varies significantly across corporate profiles, manifesting most prominently in non-state-owned enterprises, high-tech firms, and those located in eastern regions. These findings provide robust micro-level evidence for policymakers aiming to optimize digital economic systems and accelerate the systemic formation of advanced productive forces. Full article

Against the backdrop of the United Nations Sustainable Development Goals (SDGs), innovation-driven development serves as the core engine of long-term sustainable economic development, while resource misallocation has emerged as a critical bottleneck constraining sustainable innovation and coordinated regional development. Grounded in the neoclassical theory of factor allocation, this paper incorporates capital misallocation, labor misallocation and the digital economy into a unified analytical framework. Using China’s provincial panel data spanning 2001 to 2024, we systematically investigate the impact effects, underlying transmission mechanisms and regional heterogeneity of resource misallocation and the digital economy on scientific and technological innovation through benchmark regression, robustness tests and heterogeneity analysis. The results show that resource misallocation exerts a significant and robust inhibitory effect on technological innovation, with the inhibitory effect of capital misallocation being more pronounced than that of labor misallocation. The digital economy has a significant positive driving effect on technological innovation, and it can also indirectly boost technological innovation by alleviating resource misallocation, with its mitigating effect on resource misallocation presenting obvious structural differences and a stronger correction effect on capital misallocation than on labor misallocation. Economic growth and technological innovation form a mutually reinforcing “growth-innovation” virtuous cycle. In addition, the innovation effects of both resource misallocation and the digital economy exhibit significant regional heterogeneity, where the digital economy’s innovation-driven effect and misallocation-mitigating effect are notably stronger in eastern China than in the central and western regions. The theoretical contribution of this paper lies in constructing a transmission mechanism framework of “digital economy to resource misallocation to technological innovation”, which enriches the connotations of factor allocation and innovation theories. Its practical value is to provide policymakers with differentiated development paths for the digital economy and optimization strategies for factor allocation, thus facilitating the effective implementation of the innovation-driven development strategy. Full article

The generation of examination questions from examiner-provided marking schemes remains a critical yet underexplored challenge in automated assessment. This study proposes an AI-powered natural language processing (NLP) framework that reverse-engineers exam questions using transformer-based generative modeling, semantic reconstruction, and pedagogical constraints. Marking schemes are encoded with MPNet embeddings and decoded into candidate questions by a T5-small model, with a reconstruction module ensuring semantic fidelity and Bloom-level embeddings enforcing cognitive alignment. Evaluation on a dataset of 7021 marking schemes from Sol Plaatje University demonstrated strong performance, with BLEU = 0.71, ROUGE-L = 0.68, METEOR = 0.65, reconstruction fidelity = 0.84, and Bloom-level accuracy = 0.79. Comparative baselines, including an unconstrained T5 (BLEU = 0.62, RF = 0.68, Bloom = 0.56) and rule-based methods (BLEU = 0.48, RF = 0.51, Bloom = 0.43), confirmed the effectiveness of the proposed approach. The results indicate that the framework generates questions that are semantically accurate, structurally coherent, and pedagogically valid, offering a scalable solution for adaptive assessment, digital archiving, and automated exam construction. Full article

Background/Objectives: Prostate cancer testing relies on prostate-specific antigen testing and digital rectal examination, which have limited specificity and face cultural or geographic barriers to access. We developed a non-invasive urine-based liquid biopsy assay using engineered hydrogel arrays and machine learning to detect disease-specific biochemical profiles. Methods: We collected voided urine samples from 283 participants at 26 U.S. urology practices prior to prostate biopsy. Random forest classifiers trained on 184 biopsy-confirmed cancer cases and 75 controls analyzed colorimetric signatures. Results: Across all Gleason grades (6–10), the assay achieved 97.8% sensitivity and 53.3% specificity. Performance varied by grade: high-grade cancers showed 97.3% specificity, while low-to-intermediate grades demonstrated 94.0% sensitivity. Conclusions: This accessible, culturally-appropriate platform could expand prostate cancer detection in diverse populations while reducing unnecessary invasive biopsies. Full article

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

Metal additive manufacturing (MAM) enables the production of complex, high-value components for sectors such as aerospace, energy, and biomedical engineering. However, its large-scale industrial adoption remains constrained by internal defects, residual stresses, distortions, microstructural variability, and the complexity of the coupled process-parameter space. This review examines how artificial intelligence (AI)—including machine learning, deep learning, and optimization algorithms—is being applied to address these challenges across the MAM workflow. A structured literature review was conducted covering studies published between 2015 and 2025, identified through searches in Scopus, Web of Science, and IEEE Xplore. The selected literature is analyzed according to key functional domains of metal additive manufacturing: design for additive manufacturing (DfAM), process modeling and simulation, in situ monitoring and control, and microstructure and property prediction. AI approaches are further categorized by learning paradigm, including supervised learning, deep learning, reinforcement learning, and hybrid physics–machine learning models. The review highlights recent advances in AI-assisted parameter optimization, defect detection, and digital-twin frameworks for process supervision. At the same time, it identifies persistent challenges, particularly the scarcity and heterogeneity of datasets, limited transferability across machines and materials, and the need for uncertainty-aware models capable of supporting validation and certification. Overall, the analysis indicates that the integration of multi-sensor monitoring with hybrid physics-informed AI models represents the most promising near-term pathway to improve process reliability, reduce trial-and-error experimentation, and accelerate industrial qualification in metal additive manufacturing. Full article

The growing demand for high-fidelity, multi-parameter, distributed sensing in critical domains such as structural health monitoring, oil and gas exploration, and secure perimeter surveillance is pushing traditional optical fiber sensors (OFS) to their performance limits. Although conventional multiplexing techniques such as time-division and wavelength-division multiplexing (TDM, WDM) have been commercially successful, they are rapidly approaching fundamental bottlenecks in sensor density, spatial resolution, and data capacity. This review argues that the synergistic convergence of space-division multiplexing (SDM) and artificial intelligence (AI) represents a paradigm shift, enabling a new generation of intelligent, high-dimensional sensing networks. We comprehensively survey the state of the art in SDM-based OFS, detailing the operating principles and applications of multi-core fibers (MCFs) for ultra-dense sensor arrays and 3D shape sensing, as well as few-mode fibers (FMFs) for mode-division multiplexing and enhanced multi-parameter discrimination. However, the unprecedented spatial parallelism provided by SDM introduces significant challenges, including inter-channel crosstalk, complex signal demultiplexing, and massive data volumes. This paper systematically explores how AI, particularly machine learning (ML) and deep learning (DL), is being leveraged not merely as a tool but as an indispensable core technology to mitigate these impairments. We critically analyze AI’s role in digital crosstalk suppression, intelligent mode demultiplexing, signal denoising, and solving complex inverse problems for parameter estimation. Furthermore, we highlight how this AI–SDM synergy enables capabilities beyond the reach of either technology alone, such as super-resolution sensing and predictive analytics. The discussion is extended to include the critical supporting pillars of this ecosystem, such as advanced interrogation techniques and the associated data management challenges. Finally, we provide a forward-looking perspective on the trajectory of the field, outlining a path toward cognitive sensing networks that are self-calibrating, adaptive, and capable of autonomous decision-making. This review is intended to serve as a foundational reference for researchers and engineers at the intersection of photonics and intelligent systems, illuminating the pathway toward tomorrow’s intelligent sensing infrastructure. Full article

The growing implementation of Building Information Modelling (BIM) within the architecture, engineering, and construction (AEC) industries has placed increased pressure on higher education institutions to prepare graduates for interdisciplinary digital collaboration. In many emerging higher education environments, such as South Africa, structured pedagogical frameworks for BIM Level 3 collaboration are less well established. This paper addresses this gap by introducing and evaluating a seven-step BIM collaboration framework in an interdisciplinary final year undergraduate project. A comparative cohort case study design was adopted, analysing two cohorts: the 2022 cohort operating within a traditional siloed design model, and the 2023 cohort applying the proposed framework. Grounded in Habermas’s theory of communicative action, student design projects and self-reflection narratives from both the traditional siloed design process and the BIM-enabled framework were analysed deductively according to communication frequency, content, and quality as key categories. Communication quality was evaluated through intrinsic, contextual, representational, and accessibility information dimensions. Findings show that the BIM group had higher levels of established collaboration, better-quality contextually available information, more accessible structured data, and more effective communication. The findings indicate that structured BIM-based collaboration enhances a transformation from mere data exchange to constructive participation and comprehensive information development among students. Rather than functioning solely as a technical tool, BIM served as a structured communication environment that supported critical engagement and interdisciplinary workflows. This study offers a transferable pedagogical model for interdisciplinary BIM education and provides evidence supporting communication-oriented approaches to digital collaboration within built environment curricula. Full article

Industrial automation laboratories often face limitations related to restricted access to industrial equipment, safety constraints, and limited scalability for hands-on experimentation. To address these challenges, this work proposes a real-time multi-layer Digital Twin architecture integrating a physical Siemens S7-1500 PLC, an immersive Unity-based virtual environment, HMI supervision, and IoT-enabled remote monitoring within a unified communication framework. The architecture is structured into physical, digital, and integration layers, enabling modular scalability and bidirectional synchronization between the physical process and its virtual representation through Ethernet TCP/IP communication. System performance was evaluated using synchronization metrics including communication latency, jitter, deterministic timing deviation, and event synchronization accuracy. Experimental results demonstrated stable PLC–Digital Twin communication with average latencies below 15 ms and jitter below 0.5 ms, ensuring reliable real-time interaction during continuous operation. A comparative evaluation with engineering students also showed improved learning conditions, achieving high perceived usability (SUS = 86/100) and reduced cognitive workload (NASA-TLX = 34/100). These results confirm the effectiveness of the proposed architecture as a scalable platform for Industry 4.0 training environments. Full article

Three-dimensional (3D) printing, also known as additive manufacturing (AM), has become increasingly integrated into dentistry because of its high precision, efficiency, and ability to fabricate patient-specific devices. This review comprehensively discusses the historical development of 3D printing and outlines the fundamental principles of the most widely used technologies in dentistry, including stereolithography (SLA), digital light processing (DLP), and liquid crystal display (LCD). These technologies enable the accurate and efficient fabrication of dental models, crowns, bridges, dentures, surgical guides, orthodontic appliances, and tissue engineering scaffolds. Current clinical applications are systematically summarized across major dental disciplines, including prosthodontics, orthodontics, oral and maxillofacial surgery, endodontics, periodontics, and pediatric dentistry. Despite existing challenges, such as limited long-term clinical data for certain materials, high initial equipment costs, and post-processing requirements, 3D printing offers substantial advantages in terms of customization, workflow efficiency, and clinical predictability of the final product. Future developments in advanced biomaterials, artificial intelligence-assisted workflows, bioprinting, and four-dimensional (4D) printing are expected to further expand the role of additive manufacturing in personalized and regenerative dentistry. Full article