Ferry quays experience rapid deterioration due to their exposure to harsh maritime environments and operational ferry impacts. Vibration-based structural health monitoring offers a valuable approach to assessing structural integrity and enhancing the understanding of the structural implications of these impacts. However, practical limitations often restrict sensor placement at critical locations. Consequently, virtual sensing techniques become essential for establishing a Digital Twin and estimating the structural response. This study investigates the application of the Gaussian Process Latent Force Model (GPLFM) for virtual sensing through Bayesian inference on the Magerholm ferry quay, combining in-operation experimental data collected during a ferry impact with a detailed physics-based model. The proposed Physics-Encoded Machine Learning model integrates a reduced-order structural model with a data-driven GPLFM representing the unknown impact forces via their modal contributions. This formulation enables joint probabilistic inference of system states and unmeasured responses while accounting for modeling and measurement uncertainties. Results show that the GPLFM provides accurate posterior mean acceleration response estimates at most locations, even under simplifying modeling assumptions such as linear time-invariant behavior during the impact phase. Lower accuracy was observed at locations in the impact zone. A numerical study was conducted to explore an optimal real-world sensor placement strategy using a Backward Sequential Sensor Placement approach. Sensitivity analyses examined the influence of measurement noise, sensor types, incorrectly assumed damping ratios, and sampling frequencies. The results suggest that the GP latent forces can help accommodate modeling and measurement uncertainties, maintaining acceptable estimation accuracy across scenarios.

Future space-terrestrial networks demand autonomy, sustainability, and trust-aware intelligence. This paper proposes a Neuro-Symbolic Digital Twin (NSDT) architecture tailored for geostationary (GEO) satellites. By fusing neural inference with symbolic reasoning, NSDT delivers interpretable, resilient decision-making under uncertain or hostile conditions. Key innovations include a semantic trust layer, green beamforming and a novel energy metric ${\varepsilon _{NS}}$. High-fidelity MATLAB simulations demonstrate 35–40% energy savings, 3–5 dB SINR gains, 0.8–0.9 resilience, and latency of 120–160 ms versus conventional approaches.

Digital twins (DT) have the potential to transform the Architecture, Engineering, Construction and Operation (AECO) industry by improving project efficiency, decision-making and long-term asset management, yet their adoption remains fragmented due to the absence of a structured framework aligned with industry-standard workflows. Existing methodologies, such as the Royal Institute of British Architects Plan of Work and associated Overlays, provide guidance for project delivery but do not explicitly integrate DT considerations. The role of collaborative procurement (CP) in enabling DT uptake remains underexplored despite its recognised contributions to improving project outcomes. This paper establishes the rationale for a dedicated DT framework tailored to the built environment by synthesising cross-industry insights, evaluating current methodologies and identifying key characteristics that support early-stage DT integration, data continuity and long-term stakeholder collaboration. A proactive approach, anchored in CP principles, ensures DT adoption is embedded from project inception, preventing fragmented implementation and unlocking its full potential. As climate resilience and net-zero targets become fundamental industry drivers, a structured DT framework ensures that digital innovation actively contributes to improved energy performance, life cycle efficiency and broader sustainability goals. This paper reinforces the need for a scalable and adaptable framework that supports coherent DT adoption and enhances efficiency, resilience and sustainability across the AECO sector.

To solve the problems of precise operation and real-time interaction during the spraying process of industrial robots, a new spraying method based on digital twin technology is proposed. In view of the limitations of traditional spraying processes in complex geometric shape processing, spraying uniformity control, and operational flexibility, this study built a highly simulated virtual environment based on digital twin and human–machine collaboration technology, allowing operators to guide the robot in real time for precise spraying operations. The use of multisensor fusion technology achieves a high degree of consistency between the physical and virtual environments, ensuring that the system can maintain high-precision spraying on complex workpiece surfaces. The experimental designed spraying tasks for different geometric shapes and evaluated the performance of the system’s interactive spraying method in terms of real-time feedback guidance and path planning. The results show that the proposed method significantly improves the accuracy and efficiency of the spraying process, especially showing obvious advantages when processing complex geometric workpieces, and provides a new technical approach for future high-precision manufacturing.

This article examines the implications of adopting a socio-technical perspective on the design and implementation of GovTech solutions. To observe the phenomenon, it adopts a case study approach focusing on the WiseTown solution and its City Digital Twin (CDT), developed by the Italian company TeamDev. The article investigates how integrating social factors, such as urban governance, with technical elements, like data analysis and modeling, can enhance the conceptualization, design, and implementation of user-centric, data-driven digital solutions as part of a broader digital transformation strategy. The article explores an Italian best practice that is developing four dimensions of the GovTech socio-technical framework: Governance Structures, Institutional Arrangements, User and Context Understanding, and Technological Development. It critically examines and discusses the challenges and opportunities associated with the adoption of CDTs and their impact on public policy implementation. The analysis is centered on two main aspects that emerged from the case study: data integration and sharing within CDTs, and the social implications associated with data usage for decision-making. Ultimately, the article explores the role of stakeholder collaboration (public-private partnerships) and the creation of innovation ecosystems—GovTech ecosystem in this specific case—to inform and steer policymaking through and beyond the adoption of CDTs.

The paper proposes an approach called proactive design for sustainability (DfS) in the context of Industry 5.0, for human-centred innovation and environmental sustainability, combined with the technological focus of Industry 4.0. Computer Aided Design (CAD) must integrate sustainability considerations into product development, with the use of Artificial Intelligence (AI), Digital Twins (DTw) and the Internet of Things (IoT) to dynamically monitor and optimise environmental impacts during the design process, with the integration of Key Sustainability Indicators (KSI) into the CAD interface to enable informed decision-making, aligning design parameters with resource availability and environmental constraints. A case study of an autonomous mobile robot (AMR) will show how operational data from the product lifecycle, combined with AI predictions, can reduce energy consumption and emissions.

Digital Twins are digital representations of products or product-service systems comprising a Digital Master, which consists of product description models, and a Digital Shadow, which encompasses data collected throughout the product’s life cycle. To create a Digital Twin, the Digital Master and Digital Shadow must be interlinked. The Digital Master, Digital Shadow, and thus their twinning can vary in complexity and analytical capabilities. This paper introduces a systematic description of six twinning levels ranging from simple data exchange based on generic models to more complex forms targeting model parameter and Digital Twin goal optimization. The example of a valve is used for illustration. The presented description aids in understanding the potential of Digital Twins and serves as a guide to select appropriate twinning levels based on specific product requirements and use cases.

The overall quality of final Digital Twin (DT) solutions and their ability to produce useful insights are key considerations for researchers and for the industry to readily adopt them. However, validation of DTs is often neglected in existing research dedicated to their development. Further, there is a lack of methodologies for building bi-directional information exchanges between virtual and real spaces, potentially hindering effective decision-making. This work presents a comparative analysis of several quantitative metrics by implementing them on the Digital Twin of a railway braking system as a use case. Their suitability as performance measures for validation and as thresholds to support decision-making is assessed. Their integration into a novel DT structure is shown to contribute to a well-rounded validation procedure and a practical decision-making framework.

Products need to be developed faster and more efficiently, which is why companies are seeking to leverage the benefits of digitalization. A current trend is the digital twin (DT), which offers many advantages but also involves high development efforts. Research has addressed the use of the DT along the product life cycle (PLC) to compensate for the development effort, but these approaches are often imprecise and not directly applicable in industry. This paper therefore describes how the individual components of the DT can be utilized along the PLC beyond the manufacturing and use phase with a focus on product design. The resulting framework is then illustrated using a case study of a product service system. This article aims to facilitate the use of the DT in industry to improve product design across product generations.

Digital Twins are widely recognized as a transformative technological trend, yet their potential to foster innovation, particularly their generative capabilities, remains underexplored. This paper investigates how they can transcend traditional optimization roles to serve as tools for advancing knowledge and generativity in the design of their physical counterparts. Leveraging C-K theory, a framework is presented for modeling design processes with Digital Twins, characterizing design scenarios and identifying two distinct forms of generativity. An illustration of these results shows how designers can leverage Digital Twin reflexive capacity to challenge and reconfigure underlying knowledge of their physical counterparts. The transformative value of this reflexivity, combined with remodeling capabilities, is highlights the exploration of new design pathway for Digital Twins themselves.

Recent academic contributions explore the integration of Digital Twins (DTs) within smart Product-Service System (sPSS). This integration aims to innovate business propositions, hardware and services. However, gaps persist in developing DT environments to support early-stage collaborative innovation for sPSS, and limited studies explore how real-time synchronized digital replicas enhance value co-creation in this area. This paper addresses this gap by presenting a framework and practical example of integrating value-driven decision support into early sPSS conceptual design. A case study on the development of the Smart Electric Vehicle (SEV) conducted with a global automotive Original Equipment Manufacturer (OEM) demonstrates the framework’s efficacy. Through qualitative data analyses based on experimental validation in a case company, the DT proves effective in aiding decision makers in selecting value-adding configurations within specific scenarios. Furthermore, the DT serves as a visual decision-making tool, fostering collaboration across diverse teams within the automotive company. This collaboration facilitates value creation across practitioners with varied backgrounds, emphasizing the DT’s role in enhancing early-stage innovation and co-creation processes in the sPSS domain.

This paper documents the details of the design, verification, and certification of a novel technology: a remote monitoring system (digital twin) for a voyage data recorder, referred to as the HermAce Gateway. The electronic components, data transfer, and storage principle explain how the HermAce Gateway communicates and records safety-critical messages. Various prospective benefits to the industry are provided, primarily regarding the opportunities for remote support and testing that the digital twin facilitates. The HermAce Gateway was independently verified through a combination of semi-automated software in the loop and selected complimentary hardware in the loop tests. Different types of communication were simulated in multiple ways, including approximating real-world scenarios. Alarms contained in correctly formed messages were found to be detected and recorded by the HermAce Gateway, and a discussion of how this evidence can be quantified in the context of reducing uncertainty in the reliability of a digital twin. Certification of a digital system is a new concept in the maritime industry. The identification of functional requirements, which informed the verification testing, and the development of an AI register for what is expected to be an increasing number of such systems are also documented.

Digital twins are a new paradigm for our time, offering the possibility of interconnected virtual representations of the real world. The concept is very versatile and has been adopted by multiple communities of practice, policymakers, researchers, and innovators. A significant part of the digital twin paradigm is about interconnecting digital objects, many of which have previously not been combined. As a result, members of the newly forming digital twin community are often talking at cross-purposes, based on different starting points, assumptions, and cultural practices. These differences are due to the philosophical world-view adopted within specific communities. In this paper, we explore the philosophical context which underpins the digital twin concept. We offer the building blocks for a philosophical framework for digital twins, consisting of 21 principles that are intended to help facilitate their further development. Specifically, we argue that the philosophy of digital twins is fundamentally holistic and emergentist. We further argue that in order to enable emergent behaviors, digital twins should be designed to reconstruct the behavior of a physical twin by “dynamically assembling” multiple digital “components”. We also argue that digital twins naturally include aspects relating to the philosophy of artificial intelligence, including learning and exploitation of knowledge. We discuss the following four questions (i) What is the distinction between a model and a digital twin? (ii) What previously unseen results can we expect from a digital twin? (iii) How can emergent behaviours be predicted? (iv) How can we assess the existence and uniqueness of digital twin outputs?

Despite technological and medical advances, amputations continue to increase. Amputees face significant challenges when acquiring and using prosthetic devices, challenges which are made worse as their emotional needs, aspirations, mobility, prosthesis requirements and problems change over time. These challenges require custom solutions for each individual amputee, a fact that current amputee centered prosthesis services tend to ignore. The work reported in this paper contributes an AI based Prosthesis Development Service Framework to cater for the current and evolving needs of amputees.

Shorter product lifecycles are also leading to even shorter planning times for the development of production systems. In most companies, the restructuring is carried out within a few weeks during the annual holidays. Digital tools such as simulations or the digital twin are used to avoid delaying the restructuring during this time. However, the introduction of a 3D model of the factory is often the first point of failure for many companies. This article proposes a six-step process model that enables the transition from 2D to 3D design. The process model was evaluated in a research project.

Additive manufacturing (AM) became a key technology in the development of innovative products. Advancements have been made to improve economic feasibility. However, ecological sustainability is still an open issue of AM. To improve sustainability, it is crucial to track, visualize, and evaluate emissions along the lifecycle. This paper presents a novel Digital Twin based approach enabling prediction of the product carbon footprint (PCF) and prescriptive measures to improve sustainability. By improving part and process design, a significant PCF reduction was achieved.

Digital Twins are perceived differently between and within industry and academia regarding applications and potentials. For this reason, a round table was formed based on the Digital Twin Workshop of the Design Conference 2022. One of the results of this round table is this contribution, which deals with a survey within the industry. The survey captured the understanding of the different roles in the creation and use of Digital Twins, the requirements and hurdles as well as the perception of methodological support. In addition, factors that influence the perception were identified.

Digital Twins (DTs) are intended to be utilized for a wide range of applications, promising benefits like visualization, monitoring, simulation and control of a physical system. Not only the development of a DT for a production facility is a time-consuming task, but also to keep the virtual counterpart up to date in the use phase. In this work, the implementation of an industrial-scale DT of an automotive supplier production site based on a Discrete-Event Simulation (DES) model with self-optimization capabilities for easier maintainability and increased simulation accuracy is presented.