Search Results (8,144)

Digital Twin (DT) implementations in electrical and industrial systems are governed by fragmented ISO/IEC and IEC standards spanning terminology, architecture, interoperability, lifecycle management, and cybersecurity. This paper proposes a mathematical framework that integrates these standards into a unified compliance model. A layered DT architecture is defined as a finite set of functional abstractions, and standards are linked to layers through a multivalued mapping and an incidence matrix. Traceability, interoperability, fidelity, and security/governance indicators are normalized and aggregated through a bounded weighted functional to obtain a deterministic compliance score. The model is then extended by treating selected indicators as random variables, which enables probabilistic maturity classification and Monte Carlo-based robustness analysis. The resulting functional is bounded, monotone, and stable under bounded perturbations. Numerical experiments on a synthetic portfolio illustrate deterministic scoring and uncertainty effects. The framework provides a proof-of-concept basis for structured DT compliance assessment across heterogeneous electrical systems; however, broader empirical validation is still required before operational deployment. Full article

Building Information Modeling (BIM) has fundamentally changed the way interdisciplinary coordination works in construction projects; however, the theoretical mechanisms underlying open collaboration standards in this field remain insufficiently explored. This article fills this gap by presenting a systematic analysis of the BIM Collaboration Format (BCF) through the lens of reification and serialization, two fundamental concepts in information systems theory. Although the BCF format is widely used in the industry and implemented in major BIM tools for clash detection and issue tracking, the existing literature treats it primarily as an operational tool, overlooking the deeper information systems principles that govern its architecture. The analysis demonstrates that BCF achieves reification by transforming informal coordination knowledge—such as verbally communicated clashes, scattered email threads, and undocumented design decisions—into first-class objects (Topic, Comment, Viewpoint) equipped with unique identifiers, typed attributes, ownership, temporal metadata, and formalized inter-object relationships. Further analysis was conducted on BCF’s serialization mechanisms, including XML encoding for file exchange, JSON for RESTful API communication, and ZIP archiving as a distribution container, each of which was selected to balance human readability, schema validation, compression, and cross-platform portability. The complementarity of these two mechanisms was examined: reification determines what to preserve and in what structure, while serialization determines how to encode and in what format, which together enable interoperable, auditable, and automatable coordination workflows in heterogeneous software environments. The analysis was illustrated with a real-world BCF example from a major infrastructure project in Poland, demonstrating practical alignment between theoretical constructs and their implementation. The research results provide both a conceptual foundation for researchers working on openBIM standards and practical guidance for practitioners seeking to optimize issue management, the implementation of a Common Data Environment (CDE), and the specification of Exchange Information Requirements (EIR). The study contributes new knowledge in three areas: (1) To the best of the authors’ knowledge, it provides the first systematic theoretical analysis of BCF through the lens of reification and serialization, filling a gap between the format’s widespread practical use and its limited theoretical understanding. (2) It demonstrates how the formal criteria of reification (unique identity, typed attributes, ownership, temporal metadata, and inter-object relationships) map onto specific BCF entities, offering a transferable analytical framework for evaluating other openBIM standards. (3) It identifies the complementarity of reification and serialization as a design principle that can guide the development of future standards for digital twins and IoT-based facility management. Full article

Plant extracts are widely used as natural pesticides, cosmetic ingredients, and in pharmaceutical applications. However, their poor water solubility and stability limit their usability. Lipid nanoparticles (LNPs) offer an effective encapsulation strategy to overcome these challenges. This study demonstrates the encapsulation of three representative substances from these industries: quercetin as a pesticide, irones as a cosmetic ingredient, and nucleic acids for pharmaceutical use. Ultrasonic treatment was used for the encapsulation of quercetin and irones, and a concept for continuous encapsulation in a plug flow reactor was proposed for process intensification. Inline multi-angle light scattering and dynamic light scattering measurements proved effective for real-time monitoring and enabled the replacement of traditional batch measurements. In the pharmaceutical area, mRNA-based therapies require LNP encapsulation to prevent nucleic acid degradation. Plant-based β-sitosterol was used as an alternative helper lipid to cholesterol, resulting in an average particle diameter of 72 nm and an encapsulation efficiency of 91%, comparable to commercial formulations such as the Comirnaty vaccine. Furthermore, a novel process model based on population balances was developed to simulate the entire manufacturing process, from rapid mixing in a T-mixer to particle stabilization via buffer exchange during diafiltration. By applying a quantitative and distinctive model validation workflow, the model was shown to be as accurate and precise as the experimental data, enabling its use as a digital twin for autonomous continuous operation. In summary, this study contributes to reducing the facility footprint and cost of goods through the implementation of continuous processing and model-based control. This approach improves productivity by 20% and reduces process time by a factor of two. Full article

Mitral transcatheter edge-to-edge repair (M-TEER) has emerged as a cornerstone in the management of severe mitral regurgitation, serving as a robust, low-risk alternative to conventional mitral valve surgery. Although thromboembolic risk remains a critical clinical challenge, that varies significantly across the clinical continuum, from pre-procedural substrates to post-procedural management. This review highlights the role of atrial cardiomyopathy in creating a prothrombotic milieu even prior to intervention, while during the procedure, device time emerges as a potentially dominant independent predictor of embolic burden, marking the periprocedural window as the period of peak hazard. Furthermore, this article addresses the notable disparity between the near-universal presence of subclinical ischemic lesions on magnetic resonance imaging and the infrequent incidence of overt neurological deficits. As the post-procedural phase is considered, we discuss the shift from standardized antithrombotic protocols to individualized strategies and the potential role of concomitant left atrial appendage occlusion. Ultimately, integrating these stage-specific clinical and procedural determinants with emerging technologies—like digital twins and artificial intelligence—represents a promising frontier for mitigating embolic risks, optimizing procedural planning and patient safety in the evolving landscape of mitral valve interventions. Full article

The quasi-steady low-Reynolds-number flow induced by a linear chain of multiple slip spheres translating along their common axis in a Newtonian fluid is investigated. The particles are allowed to differ in radius, Navier slip coefficient, migration velocity, and interparticle spacing. A semi-analytical solution of the governing Stokes equation is obtained using a boundary collocation method. Hydrodynamic interactions among the particles are shown to be significant under appropriate geometric and surface conditions. For the two-sphere configuration, the computed hydrodynamic forces agree closely with previously published asymptotic solutions derived via the twin multipole expansion method. In the three-sphere case, the presence of a third particle substantially modifies the forces acting on the other two, demonstrating non-negligible many-body interaction effects. The interaction strength is found to be more pronounced for smaller particles or those with lower slip coefficients. Calculations for longer particle chains further reveal a clear hydrodynamic shielding effect within the assembly. Full article

Accurately and promptly acquiring the load history characteristics of impact events on composite aircraft structures is crucial for identifying impact-induced damage and developing high-fidelity digital twin models. To address this need, we propose a method for reconstructing the impact load history on composite structures, leveraging Generalized Cross-Validation (GCV) and a Fiber Bragg Grating (FBG) pattern. An equivalent expansion technique based on discretized time-domain sparse strain sampling is developed to mitigate the local distortion of impact response signals, a common issue arising from the low sampling rates of quasi-distributed FBG. By incorporating Tikhonov regularization, the ill-posed nature of the impact frequency response matrix is effectively managed. Furthermore, an adaptive optimization method based on the GCV criterion is introduced to overcome the limitations of manually selecting regularization parameters and the associated constraints on noise suppression. The results show that the proposed GCV-based reconstruction method achieves an average peak relative error of 11.4% and an average root mean square error of 0.36 N for the reconstructed impact load, demonstrating that the proposed method synergistically enhances both the reconstruction of the overall impact load waveform profile and the precise characterization of transient details, even with low-rate sampling. This provides robust technical support for health monitoring and condition-based maintenance of composite structures. Full article

This study presents a comprehensive and systematic integrative review of Neighborhood-Level Energy Hubs (NLEHs) as pivotal enablers of sustainable and resilient urban energy systems. In response to accelerating climate pressures, rapid urbanization, and the decentralization of energy production, NLEHs are conceptualized as multi-carrier platforms that enable coordinated energy generation, storage, conversion, and exchange at the neighborhood scale. Utilizing a PRISMA-informed methodology to synthesize 125 core studies, the review systematically evaluates recent advances across five interconnected dimensions: conceptual foundations, system typologies, energy flow architectures, urban integration, and optimization paradigms. Unlike conventional reviews, this study explicitly bridges the critical gap between techno-economic optimization and socio-environmental priorities. A key novelty is the proposed mathematical integration of energy justice and Social Life Cycle Assessment (S-LCA) directly into optimization algorithms (e.g., MILP and MPC) as dynamic constraints and penalty terms. Particular emphasis is placed on participatory governance models, lifecycle sustainability metrics, and digitalization tools such as AI-driven energy management systems and urban digital twins. The analysis further reveals critical research gaps, highlighting a stark geographic dichotomy between high-tech, market-driven NLEHs in the Global North and resilience-oriented hybrid microgrids in the Global South, alongside the lack of adaptive regulatory frameworks. By proposing a unified Cyber–Physical–Social perspective, this study provides actionable insights for planners, policymakers, and researchers to support the development of scalable, inclusive, and context-sensitive NLEH implementations. Ultimately, the paper contributes to redefining neighborhood-scale energy systems as not only efficient and low-carbon infrastructures, but also as socially equitable, globally scalable, and institutionally adaptive components of future smart cities. Full article

Depression is a prevalent mental health disorder characterized by persistent sadness, loss of interest, and impaired daily functioning. Wearable monitoring systems have emerged as promising tools for continuous mental health assessment; however, they face challenges such as data privacy concerns, misclassification risks, and limited ability to capture complex emotional states. To address these limitations, this study proposes a Self-Supervision-Enabled Compounded Multi-Modal Feature-Learning Network (S2-CFL) for depressive state classification using wearable sensor data and psychological self-reports. The framework integrates a Twin-Path Encoder–Decoder Network (TP-EDN) for extracting temporal features from raw signals and a Densely Connected Convolution Pyramidal Transformer Network (DC2-PTN) for learning spatial representations from signal-to-image transformations. A fusion mechanism combines multi-modal features to predict depressive states, valence, and arousal levels, while a Fine-Grained Emotion Classification Network (FGECN) is employed to categorize emotional states into multiple classes using supervised learning models. Experimental results demonstrate that the proposed multi-modal approach improves classification performance and provides interpretable insights into emotional and depressive patterns. Full article

The incorporation of artificial intelligence, multi-sensor perception, and cyber-physical control into mining operations offers tremendous opportunities for increasing productivity, safety, and sustainability. However, present frameworks focus on discrete subsystems rather than providing a unified, safety-constrained optimization method that has been verified in both surface and underground environments. This paper describes a scalable, hierarchical autonomous mining architecture that incorporates sensor fusion, edge intelligence, fleet coordination, and digital twin-based decision support. It is designed to operate in GNSS-denied conditions and extreme climatic constraints common to Nordic mining environments. A mathematical modeling approach formalizes vehicle dynamics, drilling mechanics, and multi-agent fleet coordination inside a safety-constrained multi-objective optimization formulation. The framework is validated using Monte Carlo simulation with uncertainty measurement, sensitivity analysis, and statistical hypothesis testing. The preliminary results show improvements over a typical baseline, with productivity increasing by approximately 24.3% ± 3.2%, energy consumption decreasing by 12.8% ± 2.5%, and safety risk decreasing by 48.6% ± 4.1%. A sensitivity study identifies localization accuracy, communication delay, and optimization weighting as the primary system performance drivers. The suggested framework serves as a reproducible and transferable reference model for next-generation intelligent mining systems, having direct applications to both industrial deployment and future research in autonomous resource extraction. Full article

Positive mental health (PMH) has recently become a key topic in biomedical research. Previous studies have explored the correlation between biological and psychological measures, but only a few have focused on the relationship between PMH and aging. This study aimed: (i) to explore the association between PMH and biological aging; (ii) to determine if and to what extent the observed association could be explained by shared genetic and environmental effects. A total of 401 twins (age 19–81 years, 32% male) from the Italian Twin Registry were recruited, and the twin study design was applied. A self-report psychological test battery was used to evaluate several PMH components. Blood samples were collected from participants to determine telomere length (TL) and mitochondrial DNA copy number (mtDNAcn). TL was negatively associated with attachment anxiety (r = −0.11, p = 0.037). A bivariate twin model provided heritability estimates of 0.14 (95% CI 0.001–0.43) for TL and 0.32 (0.16–0.45) for attachment anxiety, and a substantial negative genetic correlation [r_g = −0.55 (−1.00–0.00)] between them. Under the limitations of a cross-sectional study with a self-report wellbeing assessment, these results suggest that anxiety in a relationship with a partner may contribute to accelerated TL shortening, and shared genetic factors may underlie this link. Full article

Environmental disturbances from hospital demolition and construction can aerosolise pathogenic fungal spores, particularly those of Aspergillus species, posing a serious threat to immunocompromised patients. This paper presents a structured narrative review of representative case studies to evaluate the relationship between demolition activities and airborne Aspergillus exposure, with a focus on clinical risk and environmental monitoring. Three exemplar studies were selected to illustrate high-intensity short-duration demolition, prolonged mechanical demolition, and meteorologically integrated risk assessment. By examining these cases, this review identifies gaps in current knowledge, methodological limitations, and challenges in causal attribution. The analysis supports the development of a novel conceptual framework for assessing and managing Aspergillus-related risks during hospital redevelopment, offering a structured approach to future infection prevention and control strategies. This framework is intended as a conceptual tool to support evidence-informed decision-making while acknowledging the limitations inherent in a targeted narrative review rather than a systematic synthesis. Full article

In the context of new-type power system construction, digital twin has become the core technology for power transformers, supporting their full-life cycle intelligent operation and maintenance. The real-time, high-precision calculation of the internal temperature field serves as the core supporting element for realizing the real-time mapping between the physical transformer entity and its virtual twin. Aiming at the inherent defects of traditional temperature rise calculation methods, such as insufficient accuracy and an excessively long computation time, this paper proposes a simplified calculation model for the transformer temperature field. In this model, the transformer oil tank is simplified into a two-dimensional axisymmetric thermal–fluid coupled field model solved by the finite volume method (FVM). The Proper Orthogonal Decomposition (POD) technique is adopted to perform order reduction on the matrices involved in the governing equations, so as to reduce the computational degrees of freedom. Meanwhile, the radiator is equivalent to a one-dimensional thermal circuit model, and the field–circuit coupled solution is achieved through bidirectional data mapping. Temperature field calculation is carried out for a 220 kV oil-immersed transformer based on the proposed model. The results show that the average relative error between the calculated results and the experimental data is around 0.86%, while the computation time is merely 0.04% of that of the traditional three-dimensional full-scale model. Furthermore, taking the real-time overload capacity evaluation of the transformer as a case, it is verified that the proposed model can successfully support the requirements of practical engineering applications. Full article

Robust numerical frameworks are essential for the simulation, design, monitoring, and control of membrane-based separation units, particularly under highly nonlinear and industrially relevant operating conditions. In this context, a comprehensive phenomenological and numerical framework is proposed for the simulation of hollow-fiber membrane modules, incorporating coupled mass, momentum (through pressure drop), and energy transport equations. The governing equations are discretized using a rigorous orthogonal collocation formulation, and the performances of two numerical solution strategies are systematically investigated for the first time to allow the in-line and real-time implementation of the model: a steady-state approach based on the Newton–Raphson method with careful treatment of initial estimates, and a pseudotransient formulation. Particularly, an original and consistent numerical treatment is introduced for the energy balance at boundaries where the permeate flow vanishes, enabling the stable incorporation of thermal effects and Joule–Thomson phenomena. The results clearly show that the steady-state Newton–Raphson approach provides the best overall performance in terms of computational efficiency, numerical robustness, and accuracy when physically consistent initial profiles are employed. In particular, the combination of a linear initial guess and a numerical mesh constituted of four collocation points yielded the most favorable balance between convergence speed, numerical robustness, and accuracy for the base-case sensitivity analysis. For monitoring-oriented applications, the numerical choice should be weighted primarily toward computational performance once physical consistency and convergence criteria are satisfied, rather than toward maximum mesh-refinement accuracy. In this context, small differences in internal-fiber profiles can be compensated through real-time permeance estimation and are negligible when compared with measurement uncertainty in real industrial processes. Under extreme operating conditions involving low concentrations, low flow rates, and highly permeable species, the pseudotransient formulation proved to be a reliable auxiliary strategy, enabling robust convergence when suitable initial guesses were not readily available. The proposed framework is validated against experimental data from the literature and subjected to extensive convergence and sensitivity analyses, providing a reliable basis for simulation and for assessing computational feasibility in in-line and real-time monitoring-oriented applications. A full demonstration of digital-twin integration, online parameter updating, reduced-order coupling, and closed-loop control is beyond the scope of the present study and will be addressed in future work. Full article

Digital twins (DTs) enable innovation across industries. While business discourse promotes DTs as catalysts for new business models, the academic literature lacks a cohesive understanding of how DTs enable different types of business innovation and what distinguishes cross-organizational innovation from firm-level innovation. This paper conducts a systematic literature review of 60 articles, analyzing 25 business innovation cases through a typology derived from established frameworks extended to address cross-organizational innovation. Process innovation appeared in nearly all the cases (24 of 25), confirming DTs’ fundamental role as operational technology. Product innovation manifests in two patterns: the twin as offering and the twin enabling offerings. paradigm innovation appeared in over half of cases, taking context-specific forms including business model transformation, governance mechanisms, and organizational restructuring. Beyond-firm innovation clusters in healthcare, smart cities, sustainability transitions, and energy systems where cross-organizational coordination is required. Beyond-firm cases consistently co-occur with paradigm innovation and exhibit higher innovation type diversity than single-firm cases, suggesting that cross-boundary coordination requires accompanying organizational restructuring. The study contributes a Digital Twin Innovation Typology extending established frameworks to capture innovation no single firm can achieve alone. Practical implications address how domain context shapes innovation potential and coordination mechanisms required for beyond-firm innovation. Full article

Infrared focal plane arrays (IRFPAs) often suffer from spatiotemporal nonuniformity that persists after conventional two-point nonuniformity correction (NUC), especially under temperature drift and time-varying readout conditions. These residuals are typically structured, including column-group striping caused by shared column-end circuits and row-wise baseline/common-mode drift induced by row-scanning paths. We propose a structured, digital-twin-inspired detector-side refinement of two-point NUC that augments the bias term with interpretable low-dimensional components: a static column bias vector capturing group-correlated residuals and a row-related structured term consisting of a static row baseline and a frame-synchronous common-mode component with row-dependent sensitivity, while keeping the two-point gain/offset backbone unchanged. Rather than representing a full system-level digital twin of the infrared payload, the proposed framework serves as a detector-side virtual representation of dominant readout-induced structured residual states that can be estimated and updated from calibration data. Experiments on blackbody calibration data across multiple temperature points demonstrate that the column-related structured component significantly reduces group-wise column residuals, the row-related structured component suppresses time-varying row striping, and the combined method improves both column- and row-direction metrics consistently across temperatures. Full article