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Background: The rapid advancement of medical technologies presents significant challenges for researchers and practitioners. While traditional clinical trials remain the gold standard, they are often limited by high costs, lengthy durations, and ethical constraints. In contrast, in-silico trials and digital twins have emerged not only as efficient and ethical alternatives but also as a complementary technology that can extend beyond classical trials to predict and design new strategies. The successful application of digital twins in industries like nuclear energy, automotive engineering, and aviation underscores their potential in human health., Methods: In April 2024, Duke University hosted the first international summit on Virtual Imaging Trials in Medicine (VITM). The summit brought together over 130 experts from academia, industry, and regulatory bodies to discuss the latest developments, challenges, and future directions in this field. The event featured plenary speakers, presentations, and panel discussions, emphasizing the integration of clinical and in-silico methods to enhance medical evaluations., Results: Key takeaways included the necessity of diverse and realistic digital patient representations, the integration of physics and biology in simulations, and the development of robust validation frameworks. The summit also highlighted the importance of regulatory science and the establishment of Good Simulation Practices to ensure the credibility and reliability of virtual trials., Conclusion: The key discussions and insights from the VITM summit underscore the potential of in-silico trials to revolutionize medical research and patient care through personalized, efficient, and ethical evaluation methods. The collaborative efforts and recommendations from this summit aim to drive future advancements in virtual imaging trials in medicine., (© 2024 American Association of Physicists in Medicine.)

We explore an augmented democracy system built on off-the-shelf large language models (LLMs) fine-tuned to augment data on citizens' preferences elicited over policies extracted from the government programmes of the two main candidates of Brazil's 2022 presidential election. We use a train-test cross-validation set-up to estimate the accuracy with which the LLMs predict both: a subject's individual political choices and the aggregate preferences of the full sample of participants. At the individual level, we find that LLMs predict out of sample preferences more accurately than a 'bundle rule', which would assume that citizens always vote for the proposals of the candidate aligned with their self-reported political orientation. At the population level, we show that a probabilistic sample augmented by an LLM provides a more accurate estimate of the aggregate preferences of a population than the non-augmented probabilistic sample alone. Together, these results indicate that policy preference data augmented using LLMs can capture nuances that transcend party lines and represents a promising avenue of research for data augmentation. This article is part of the theme issue 'Co-creating the future: participatory cities and digital governance'.

Atrial fibrillation (AF) is a complex condition caused by various underlying pathophysiological disorders and is the most common heart arrhythmia worldwide, affecting 2 % of the European population. This prevalence increases with age, imposing significant financial, economic, and human burdens. In Europe, stroke is the second leading cause of death and the primary cause of disability, with numbers expected to rise due to ageing and improved survival rates. Functional recovery from AF-related stroke is often unsatisfactory, leading to prolonged hospital stays, severe disability, and high mortality. Despite advances in AF and stroke research, the full pathophysiological and management issues between AF and stroke increasingly need innovative approaches such as artificial intelligence (AI) and machine learning (ML). Current risk assessment tools focus on static risk factors, neglecting the dynamic nature of risk influenced by acute illness, ageing, and comorbidities. Incorporating biomarkers and automated ECG analysis could enhance pathophysiological understanding. This paper highlights the need for personalised, integrative approaches in AF and stroke management, emphasising the potential of AI and ML to improve risk prediction, treatment personalisation, and rehabilitation outcomes. Further research is essential to optimise care and reduce the burden of AF and stroke on patients and healthcare systems., Competing Interests: Declaration of competing interest There is no conflicts of interest., (Copyright © 2024. Published by Elsevier Inc.)

Background and Objective: Numerical simulations are valuable tools for studying cardiac arrhythmias. Not only do they complement experimental studies, but there is also an increasing expectation for their use in clinical applications to guide patient-specific procedures. However, numerical studies that solve the reaction-diffusion equations describing cardiac electrical activity remain challenging to set up, are time-consuming, and in many cases, are prohibitively computationally expensive for long studies. The computational cost of cardiac simulations of complex models on anatomically accurate structures necessitates parallel computing. Graphics processing units (GPUs), which have thousands of cores, have been introduced as a viable technology for carrying out fast cardiac simulations, sometimes including real-time interactivity. Our main objective is to increase the performance and accuracy of such GPU implementations while conserving computational resources., Methods: In this work, we present a compression algorithm that can be used to conserve GPU memory and improve efficiency by managing the sparsity that is inherent in using Cartesian grids to represent cardiac structures directly obtained from high-resolution MRI and mCT scans. Furthermore, we present a discretization scheme that includes the cross-diagonal terms in the computational cell to increase numerical accuracy, which is especially important for simulating thin tissue sections without the need for costly mesh refinement., Results: Interactive WebGL simulations of atrial/ventricular structures (on PCs, laptops, tablets, and phones) demonstrate the algorithm's ability to reduce memory demand by an order of magnitude and achieve calculations up to 20x faster. We further showcase its superiority in slender tissues and validate results against experiments performed in live explanted human hearts., Conclusions: In this work, we present a compression algorithm that accelerates electrical activity simulations on realistic anatomies by an order of magnitude (up to 20x), thereby allowing the use of finer grid resolutions while conserving GPU memory. Additionally, improved accuracy is achieved through cross-diagonal terms, which are essential for thin tissues, often found in heart structures such as pectinate muscles and trabeculae, as well as Purkinje fibers. Our method enables interactive simulations with even interactive domain boundary manipulation (unlike finite element/volume methods). Finally, agreement with experiments and ease of mesh import into WebGL paves the way for virtual cohorts and digital twins, aiding arrhythmia analysis and personalized therapies., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Published by Elsevier B.V.)

Introduction: In recent years, the proliferation of Industrial Internet of Things (IIoT) devices has resulted in a substantial increase in data generation across various domains, including the nascent 6G networks. Digital Twins (DTs), serving as virtual replicas of physical entities, have gained popularity within the realm of IoT due to their capacity to simulate and optimize physical systems in a cost-effective manner. Nonetheless, the security of DTs and the safeguarding of the sensitive data they generate have emerged as paramount concerns. Fortunately, the Federated Fearning (FL) system has emerged as a promising solution to address the challenge of data privacy within DTs. Nonetheless, the requisite acquisition of a significant volume of labeled data for training purposes poses a formidable challenge, particularly in a DT environment that blends real and virtual data., Objectives: To tackle this challenge, this study presents an innovative Semi-supervised FL (SSFL) framework designed to overcome the scarcity of labeled data through the strategic utilization of pseudo-labels., Methods: Specifically, our proposed SSFL algorithm, named SSFL-MBE, introduces a novel approach by combining Mix data augmentation and Bayesian Estimation consistency regularization loss, thereby integrating robust augmentation techniques to enhance model generalization. Furthermore, we introduce a Bayesian-estimated pseudo-label loss that leverages prior probabilistic knowledge to enhance model performance. Our investigation focuses particularly on a demanding scenario where labeled and unlabeled data are segregated across disparate locations, specifically, the server and various clients., Results: Comprehensive evaluations conducted on CIFAR-10 and MNIST datasets conclusively demonstrate that our proposed algorithm consistently surpasses mainstream SSFL baseline models, exhibiting an enhancement in model performance ranging from 0.5% to 1.5%., Conclusion: Overall, this work contributes to the development of more efficient and secure approaches for model training in DT-empowered FL settings, which is crucial for the deployment of IIoTs in 6G-enabled environments., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023. Published by Elsevier B.V.)

Introduction: This study aims to identify optimal digital twin policies for enhancing renewable energy projects. Through a comprehensive analysis, the research evaluates the potential of digital twins in the renewable energy sector while considering triple bottom line perspectives., Objectives: The study's main goal is to prioritize digital twin policies that can effectively boost renewable energy projects. The research aims to demonstrate the practical application and reliability of a proposed evaluation model., Methods: Nine criteria, derived from literature review and triple bottom line viewpoints, are selected. Using the decision-making trial and evaluation laboratory (DEMATEL) methodology and Quantum picture fuzzy rough sets, criteria weights are determined. Quantum picture fuzzy technique for order of preference by similarity to ideal solution (TOPSIS) evaluates sustainable industrial internet of things strategies in new-gen energy investments. VIsekriterijumska optimizcija i KOmpromisno Resenje (VIKOR) methodology enables a comparative assessment, and sensitivity analysis is conducted across nine cases., Results: Consistent outcomes across various methods validate the model's reliability. Ecosystem preservation carries the highest weight (0.1147), followed by resource policy optimization with digital twins (0.1139). Distributed energy resilience ranks first (RCi 0.576), closely followed by energy efficiency optimization (RCi 0.542)., Conclusion: This study underscores ecosystem preservation and efficient resource policies as pivotal for successful digital twin deployment in renewable energy projects. The findings highlight digital twins' potential contribution to environmental protection and ecosystem sustainability, emphasizing resource efficiency through their effective use., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023. Published by Elsevier B.V.)

Introduction: The concept of the metaverse, a virtual world where users can interact with a computer-generated environment, has received significant attention recently., Objectives: This study aims to investigate the application and fundamental technologies of Digital Twins (DT) in the development of the industrial metaverse, to enhance factory production efficiency., Methods: The study adopts a literature review approach to explore the architecture and key technologies of the industrial metaverse, including DT, cloud rendering, virtual-real interaction, big data visualization, and the Internet of Things. Firstly, this study analyzes the architecture and key technologies of the Industrial Metaverse, which encompass DT, cloud rendering, virtual-reality interaction, big data visualization, and the Internet of Things (IoT). These technologies are essential components in enabling the transformation from traditional manufacturing paradigms to intelligent manufacturing within the industrial metaverse. Secondly, this study investigates the key technological aspects that support DT in the industrial metaverse. It highlights the crucial role played by DT in facilitating the advancement and realization of intelligent manufacturing., Results: The study finds that DT plays a crucial role in the industrial metaverse, enabling the creation of virtual replicas of physical assets that can be monitored and analyzed in real-time., Conclusion: The development of the industrial metaverse holds significant potential for the industrial sector, providing a platform for optimizing and improving manufacturing processes., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023. Published by Elsevier B.V.)

Digital twins (DTs) are an emerging capability in additive manufacturing (AM), set to revolutionize design optimization, inspection, in situ monitoring, and root cause analysis. AM DTs typically incorporate multimodal data streams, ranging from machine toolpaths and in-process imaging to X-ray CT scans and performance metrics. Despite the evolution of DT platforms, challenges remain in effectively inspecting them for actionable insights, either individually or in a multidisciplinary, geographically distributed team setting. Quality assurance, manufacturing departments, pilot labs, and plant operations must collaborate closely to reliably produce parts at scale. This is particularly crucial in AM where complex structures require a collaborative and multidisciplinary approach. Additionally, the large-scale data originating from different modalities and their inherent 3D nature pose significant hurdles for traditional 2D desktop-based inspection methods. To address these challenges and increase the value proposition of DTs, we introduce a novel virtual reality (VR) framework to facilitate collaborative and real-time inspection of DTs in AM. This framework includes advanced features for intuitive alignment and visualization of multimodal data, visual occlusion management, streaming large-scale volumetric data, and collaborative tools, substantially improving the inspection of AM components and processes to fully exploit the potential of DTs in AM., Competing Interests: Declarations. Competing interests: The authors declare no competing interests., (© 2024. The Author(s).)

This article discusses the issue of the joint use of neural network algorithms for data processing and deterministic mathematical models. The use of a new approach is proposed, to determine the discrepancy between data from a vibration monitoring system of the cutting process and the calculated data obtained by modeling mathematical models of the digital twin system of the cutting process. This approach is justified by the fact that some coordinates for the state of the cutting process cannot be measured, and the vibration signals measured by the vibration monitoring system (the vibration acceleration of the tip of the cutting tool) are subject to external disturbing influences. Both the experimental method and the Matlab 2022b simulation method were used as research methods. The experimental research method is based on the widespread use of modern analog vibration transducers, the signals from which undergo the process of digitalization and further processing in order to identify arrays of additional information required for virtual digital twin models. The results obtained allow us to formulate a new conceptual approach to the construction of systems for determining the degree of cutting tool wear, based on the joint use of computational virtual models of the digital twin system and data obtained from the vibration monitoring system of the cutting process., Competing Interests: The authors declare no conflicts of interest.

There is an ongoing debate about the ethics of research on lifespan extension: roughly, using medical technologies to extend biological human lives beyond the current "natural" limit of about 120 years. At the same time, there is an exploding interest in the use of artificial intelligence (AI) to create "digital twins" of persons, for example by fine-tuning large language models on data specific to particular individuals. In this paper, we consider whether digital twins (or digital doppelgängers, as we refer to them) could be a path toward a kind of life extension-or more precisely, a kind of person extension-that does not rely on biological continuity. We discuss relevant accounts of consciousness and personal identity and argue that digital doppelgängers may at least help us achieve some of the aims or ostensible goods of person-span extension, even if they may not count as literal extensions of our personhood on dominant philosophical accounts. We also consider relational accounts of personhood and discuss how digital doppelgängers may be able to extend personhood in a relational sense, or at least secure some of the goods associated with relevant relationships. We conclude by suggesting that a research program to investigate such issues is relevant to ongoing debates about the ethics of extending the human lifespan.

This review examines the significant influence of Digital Twins (DTs) and their variant, Digital Human Twins (DHTs), on the healthcare field. DTs represent virtual replicas that encapsulate both medical and physiological characteristics-such as tissues, organs, and biokinetic data-of patients. These virtual models facilitate a deeper understanding of disease progression and enhance the customization and optimization of treatment plans by modeling complex interactions between genetic factors and environmental influences. By establishing dynamic, bidirectional connections between the DTs of physical objects and their digital counterparts, these technologies enable real-time data exchange, thereby transforming electronic health records. Leveraging the increasing availability of extensive historical datasets from clinical trials and real-world sources, AI models can now generate comprehensive predictions of future health outcomes for specific patients in the form of AI-generated DTs. Such models can also offer insights into potential diagnoses, disease progression, and treatment responses. This remarkable progression in healthcare paves the way for precision medicine and personalized health, allowing for high-level individualized medical interventions and therapies. However, the integration of DTs into healthcare faces several challenges, including data security, accessibility, bias, and quality. Addressing these obstacles is crucial to realizing the full potential of DHTs, heralding a new era of personalized, precise, and accurate medicine.

Major Depressive Disorder (MDD) is a complex, heterogeneous condition affecting millions worldwide. Computational neuropsychiatry offers potential breakthroughs through the mechanistic modeling of this disorder. Using the Kolmogorov theory (KT) of consciousness, we developed a foundational model where algorithmic agents interact with the world to maximize an Objective Function evaluating affective valence. Depression, defined in this context by a state of persistently low valence, may arise from various factors-including inaccurate world models (cognitive biases), a dysfunctional Objective Function (anhedonia, anxiety), deficient planning (executive deficits), or unfavorable environments. Integrating algorithmic, dynamical systems, and neurobiological concepts, we map the agent model to brain circuits and functional networks, framing potential etiological routes and linking with depression biotypes. Finally, we explore how brain stimulation, psychotherapy, and plasticity-enhancing compounds such as psychedelics can synergistically repair neural circuits and optimize therapies using personalized computational models.

Creating multi-level digital-twin models for mechanical ventilation requires a detailed estimation of regional lung volume. An accurate generic map between 2D chest surface motion and 3D regional lung volume could provide improved regionalisation and clinically acceptable estimates localising lung damage. This work investigates the relationship between CT lung volumes and the forced vital capacity (FVC) a surrogate of tidal volume proven linked to 2D chest motion. In particular, a convolutional neural network (CNN) with U-Net architecture is employed to build a lung segmentation model using a benchmark CT scan dataset. An automated thresholding method is proposed for image morphology analysis to improve model performance. Finally, the trained model is applied to an independent CT dataset with FVC measurements for correlation analysis of CT lung volume projection to lung recruitment capacity. Model training results show a clear improvement of lung segmentation performance with the proposed automated thresholding method compared to a typically suggested fixed value selection, achieving accuracy greater than 95% for both training and independent validation sets. The correlation analysis for 160 patients shows a good correlation of R squared value of 0.73 between the proposed 2D volume projection and the FVC value, which indicates a larger and denser projection of lung volume relative to a greater FVC value and lung recruitable capacity. The overall results thus validate the potential of using non-contact, non-invasive 2D measures to enable regionalising lung mechanics models to equivalent 3D models with a generic map based on the good correlation. The clinical impact of improved lung mechanics digital twins due to regionalising the lung mechanics and volume to specific lung regions could be very high in managing mechanical ventilation and diagnosing or locating lung injury or dysfunction based on regular monitoring instead of intermittent and invasive lung imaging modalities., (Creative Commons Attribution license.)

Autonomous methods to align beamlines can decrease the amount of time spent on diagnostics, and also uncover better global optima leading to better beam quality. The alignment of these beamlines is a high-dimensional expensive-to-sample optimization problem involving the simultaneous treatment of many optical elements with correlated and nonlinear dynamics. Bayesian optimization is a strategy of efficient global optimization that has proved successful in similar regimes in a wide variety of beamline alignment applications, though it has typically been implemented for particular beamlines and optimization tasks. In this paper, we present a basic formulation of Bayesian inference and Gaussian process models as they relate to multi-objective Bayesian optimization, as well as the practical challenges presented by beamline alignment. We show that the same general implementation of Bayesian optimization with special consideration for beamline alignment can quickly learn the dynamics of particular beamlines in an online fashion through hyperparameter fitting with no prior information. We present the implementation of a concise software framework for beamline alignment and test it on four different optimization problems for experiments on X-ray beamlines at the National Synchrotron Light Source II and the Advanced Light Source, and an electron beam at the Accelerator Test Facility, along with benchmarking on a simulated digital twin. We discuss new applications of the framework, and the potential for a unified approach to beamline alignment at synchrotron facilities., (open access.)

Purpose: The study explores the evolving landscape of cataract diagnosis, focusing on both traditional methods and innovative technological integrations. It aims to address challenges with subjectivity in traditional cataract grading and to evaluate how new technologies can enhance diagnostic accuracy and accessibility., Methods: The research introduces and examines the use of Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) in automating and improving cataract screening processes. It also explores the role of the Metaverse, Digital Twins, and Teleophthalmology for immersive patient education, real-time virtual replicas of eyes, and remote access to specialized care., Results: Various ML and DL techniques demonstrated significant accuracy in cataract detection. The integration of these technologies, along with the Metaverse, Digital Twins, and Teleophthalmology, provides a comprehensive framework for accurate and accessible cataract diagnosis., Conclusion: There is a notable paradigm shift toward individualized, predictive, and transformative eye care. The advancements in technology address existing diagnostic challenges and mitigate the shortage of ophthalmologists by extending high-quality care to underserved regions. These developments pave the way for improved cataract management and broader accessibility.

To date, the public health system has been impacted by the increasing costs of many diagnostic and therapeutic pathways due to limited resources. At the same time, we are constantly seeking to improve these paths through approaches aimed at personalized medicine. To achieve the required levels of diagnostic and therapeutic precision, it is necessary to integrate data from different sources and simulation platforms. Today, artificial intelligence (AI), machine learning (ML), and predictive computer models are more efficient at guiding decisions regarding better therapies and medical procedures. The evolution of these multiparametric and multimodal systems has led to the creation of digital twins (DTs). The goal of our review is to summarize AI applications in discovering new immunotherapies and developing predictive models for more precise immunotherapeutic decision-making. The findings from this literature review highlight that DTs, particularly predictive mathematical models, will be pivotal in advancing healthcare outcomes. Over time, DTs will indeed bring the benefits of diagnostic precision and personalized treatment to a broader spectrum of patients.

This paper showcases the successful development and implementation of two Digital Twin prototypes within the Lab Digital Twins project, designed to enhance the efficiency and quality control of Aigües de Barcelona's drinking water network. The first prototype focuses on asset management, using (near) real-time data and statistical models, and achieving a 70% success rate in predicting pump station failures 137 days in advance. The second prototype addresses water quality monitoring, leveraging machine learning to accurately forecast trihalomethane levels at key points in the distribution system, and enabling proactive water quality management strategies, ensuring compliance with stringent safety standards and safeguarding public health. The paper details the methodology of both prototypes, highlighting their potential to revolutionize water network management. PRACTITIONER POINTS: Digital representation of assets and processes in the drinking water treatment network Early fault detection in assets, and predictions of trihalomethane formation in the drinking water distribution network Reduction on monitoring time and incident response for target assets by means of Digital Twins Improvement in visualization, prediction, and proactive measures for asset management and water quality control Contribution to the growing knowledge on Digital Twins and their potential to revolutionize water network operations., (© 2024 The Author(s). Water Environment Research published by Wiley Periodicals LLC on behalf of Water Environment Federation.)

Background: The increased application of immune checkpoint inhibitors (ICIs) targeting PD-1/PD-L1 in lung cancer treatment generates clinical need to reliably predict individual patients' treatment outcomes., Methods: To bridge the prediction gap, we examine four different mathematical models in the form of ordinary differential equations, including a novel delayed response model. We rigorously evaluate their individual and combined predictive capabilities with regard to the patients' progressive disease (PD) status through equal weighting of model-derived outcome probabilities., Results: Fitting the complete treatment course, the novel delayed response model (R 2 =0.938) outperformed the simplest model (R 2 =0.865). The model combination was able to reliably predict patient PD outcome with an overall accuracy of 77% (sensitivity = 70%, specificity = 81%), solely through calibration with primary tumor longest diameter measurements. It autonomously identified a subset of 51% of patients where predictions with an overall accuracy of 81% (sensitivity = 81%, specificity = 81%) can be achieved. All models significantly outperformed a fully data-driven machine learning-based approach., Implications: These modeling approaches provide a dynamic baseline framework to support clinicians in treatment decisions by identifying different treatment outcome trajectories with already clinically available measurement data., Limitations and Future Directions: Conjoint application of the presented approach with other predictive tools and biomarkers, as well as further disease information (e.g. metastatic stage), could further enhance treatment outcome prediction. We believe the simple model formulations allow widespread adoption of the developed models to other cancer types. Similar models can easily be formulated for other treatment modalities., Competing Interests: Declaration of competing interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

Background: Bridges are vital construction infrastructures that almost every nation needs in order to function. Climate change is a significant issue, that especially affects the construction industry. It is very important that bridges are able to withstand the impacts of climate change and adaptation measures will be required to achieve this., Methods: The digital twin will be developed using BIM to manage the climate change adaptation measures for the bridges. A 6D BIM model will be created that includes the 3D Revit model of a bridge featuring climate change measures, the climate change adaptation measures timeline schedule, climate change adaptation cost estimation, and carbon emission estimation, which will be produced using Revit software, Navisworks, and Granta EduPack. The results will show how 6D BIM can be used to support the adaptation of bridges to the effects of climate change., Results: The findings underscore the efficacy of 6D BIM in enhancing bridge resilience against climate change impacts. The 3D model demonstrates integration of adaptation measures without compromising bridge functionality. Moreover, the 4D model's timeline scheduling facilitates hazard anticipation, project planning, communication enhancement, collaborative efforts, and project visualization. Cost estimations from the 5D model reveal varying costs among adaptation measures, while the 6D model highlights differences in carbon footprints. These BIM dimensions enable stakeholders to analyse effects on project costs and energy consumption, aiding sustainability and cost-efficiency considerations., Conclusions: The study exhibits the literature review analysis, the risk assessment, research on climate change adaptation strategies, and implementation using Revit 2022, Navisworks 2022, and Granta EduPack software. By contributing to the adaptation of bridges to climate change effects, the research has provided valuable insights and practical implications for enhancing bridge resilience globally., Competing Interests: No competing interests were disclosed., (Copyright: © 2024 Kaewunruen S et al.)

Digital twins, which are in silico replications of an individual and its environment, have advanced clinical decision-making and prognostication in cardiovascular medicine. The technology enables personalized simulations of clinical scenarios, prediction of disease risk, and strategies for clinical trial augmentation. Current applications of cardiovascular digital twins have integrated multi-modal data into mechanistic and statistical models to build physiologically accurate cardiac replicas to enhance disease phenotyping, enrich diagnostic workflows, and optimize procedural planning. Digital twin technology is rapidly evolving in the setting of newly available data modalities and advances in generative artificial intelligence, enabling dynamic and comprehensive simulations unique to an individual. These twins fuse physiologic, environmental, and healthcare data into machine learning and generative models to build real-time patient predictions that can model interactions with the clinical environment to accelerate personalized patient care. This review summarizes digital twins in cardiovascular medicine and their potential future applications by incorporating new personalized data modalities. It examines the technical advances in deep learning and generative artificial intelligence that broaden the scope and predictive power of digital twins. Finally, it highlights the individual and societal challenges as well as ethical considerations that are essential to realizing the future vision of incorporating cardiology digital twins into personalized cardiovascular care., (© The Author(s) 2024. Published by Oxford University Press on behalf of the European Society of Cardiology. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)

Digital twins offer potential to enhance the precision and personalisation of healthcare delivery. As part of a collaborative project between La Trobe University and the Murdoch Children's Research Institute, Melbourne, Australia, multidimensional 'omics-based digital twins of children are being developed. The aim is to explore their application to a range of health contexts in children. A pilot project is commencing that focuses on food allergy diagnosed at one year of age.

Vaccine manufacturing fosters the prevention, control, and eradication of infectious diseases. Recombinant DNA and in vitro (IVT) mRNA vaccine manufacturing technologies were enforced to combat the recent pandemic. Despite the impact of these technologies, there exists no scientific announcement that compares them. Digital Shadows are employed in this study to simulate each technology, investigating root cause deviations, technical merits, and liabilities, evaluating cost scenarios. Under this lens we provide an unbiased, advanced comparative technoeconomic study, one that determines which of these manufacturing platforms are suited for the two types of vaccines considered (monoclonal antibodies or antigens). We find recombinant DNA technology to exhibit higher Profitability Index due to lower capital and starting material requirements, pertaining to lower Minimum Selling Price per Dose values, delivering products of established quality. However, the potency of the mRNA, the streamlined and scalable synthetic processes involved and the raw material availability, facilitate faster market penetration and product flexibility, constituting these vaccines preferable whenever short product development cycles become a necessity., (© 2024. The Author(s).)

Scientific advancement is universally based on the dynamic interplay between theoretical insights, modeling, and experimental discoveries. However, this feedback loop is often slow, including delayed community interactions and the gradual integration of experimental data into theoretical frameworks. This challenge is particularly exacerbated in domains dealing with high-dimensional object spaces, such as molecules and complex microstructures. Hence, the integration of theory within automated and autonomous experimental setups, or theory in the loop-automated experiment, is emerging as a crucial objective for accelerating scientific research. The critical aspect is to use not only theory but also on-the-fly theory updates during the experiment. Here, we introduce a method for integrating theory into the loop through Bayesian conavigation of theoretical model space and experimentation. Our approach leverages the concurrent development of surrogate models for both simulation and experimental domains at the rates determined by latencies and costs of experiments and computation, alongside the adjustment of control parameters within theoretical models to minimize epistemic uncertainty over the experimental object spaces. This methodology facilitates the creation of digital twins of material structures, encompassing both the surrogate model of behavior that includes the correlative part and the theoretical model itself. While being demonstrated here within the context of functional responses in ferroelectric materials, our approach holds promise for broader applications, such as the exploration of optical properties in nanoclusters, microstructure-dependent properties in complex materials, and properties of molecular systems.

Conventional patient monitoring methods require skin-to-skin contact, continuous observation, and long working shifts, causing physical and mental stress for medical professionals. Remote patient monitoring (RPM) assists healthcare workers in monitoring patients distantly using various wearable sensors, reducing stress and infection risk. RPM can be enabled by using the Digital Twins (DTs)-based Internet of Robotic Things (IoRT) that merges robotics with the Internet of Things (IoT) and creates a virtual twin (VT) that acquires sensor data from the physical twin (PT) during operation to reflect its behavior. However, manual navigation of PT causes cognitive fatigue for the operator, affecting trust dynamics, satisfaction, and task performance. Also, operating manual systems requires proper training and long-term experience. This research implements autonomous control in the DTs-based IoRT to remotely monitor patients with chronic or contagious diseases. This work extends our previous paper that required the user to manually operate the PT using its VT to collect patient data for medical inspection. The proposed decision-making algorithm enables the PT to autonomously navigate towards the patient's room, collect and transmit health data, and return to the base station while avoiding various obstacles. Rather than manually navigating, the medical personnel direct the PT to a specific target position using the Menu buttons. The medical staff can monitor the PT and the received sensor information in the pre-built virtual environment (VE). Based on the operator's preference, manual control of the PT is also achievable. The experimental outcomes and comparative analysis verify the efficiency of the proposed system.

A digital twin (DT) is a virtual/digital model of any physical object (physical twin), interconnected through data exchange. In the context of Industry 4.0, DTs are integral to intelligent automation driving innovation at scale by providing significant improvements in precision, flexibility, and real-time responsiveness. A critical challenge in developing DTs is achieving a model that reflects real-time conditions with precision and flexibility. This paper focuses on evaluating latency and accuracy, key metrics for assessing the efficacy of a DT, which often hinder scalability and adaptability in robotic applications. This article presents a comprehensive framework for developing DTs using Unity and Robot Operating System (ROS) as the main layers of digitalization and communication. The MoveIt package was used for motion planning and execution for the robotic arm, showcasing the framework's versatility independent of proprietary constraints. Leveraging the versatility and open-source nature of these tools, the framework ensures interoperability, adaptability, and scalability, crucial for modern smart manufacturing applications. Our approach was validated by conducting extensive accuracy and latency tests. We measured latency by timestamping messages exchanged between the physical and digital twin, achieving a latency of 77.67 ms. Accuracy was assessed by comparing the joint positions of the DT and the physical robotic arm over multiple cycles, resulting in an accuracy rate of 99.99%. The results highlight the potential of DTs in enhancing operational efficiency and decision-making in manufacturing environments.

Current research in nephrology is increasingly focused on elucidating the complexity inherent in tightly interwoven molecular systems and their correlation with pathology and related therapeutics, including dialysis and renal transplantation. Rapid advances in the omics sciences, medical device sensorization, and networked digital medical devices have made such research increasingly data centered. Data-centric science requires the support of computationally powerful and sophisticated tools able to handle the overflow of novel biomarkers and therapeutic targets. This is a context in which artificial intelligence (AI) and, more specifically, machine learning (ML) can provide a clear analytical advantage, given the rapid advances in their ability to harness multimodal data, from genomic information to signal, image and even heterogeneous electronic health records (EHR). However, paradoxically, only a small fraction of ML-based medical decision support systems undergo validation and demonstrate clinical usefulness. To effectively translate all this new knowledge into clinical practice, the development of clinically compliant support systems based on interpretable and explainable ML-based methods and clear analytical strategies for personalized medicine are imperative. Intelligent nephrology, that is, the design and development of AI-based strategies for a data-centric approach to nephrology, is just taking its first steps and is by no means yet close to its coming of age. These first steps are not even homogeneously taken, as a digital divide in access to technology has become evident between developed and developing countries, also affecting underrepresented minorities. With all this in mind, this editorial aim to provide a selective overview of the current use of AI technologies in nephrology and heralds the "Artificial Intelligence in Nephrology" special issue launched by BMC Nephrology., (© 2024. The Author(s).)

AI and robotics aim to transform workplace landscapes in a several sectors such as manufacturing, logistics, healthcare, construction, agriculture, and education. Central to this evolution is the innovative use of Digital Twin technology, which creates real-time updated virtual replicas of physical systems and entities. This technology is especially transformative in healthcare and education, promising customized and efficient experiences for all involved. This paper outlines the AI4Work project's approach to leveraging Digital Twin Technology to improve work environments in these sectors. The goal of AI4Work is to formulate a workplace where AI and robots seamlessly collaborate with humans, while explores how to best share tasks between humans and machines in six different domains. For healthcare, AI4Work will explore how Digital Twin technology can assist occupational doctors and psychologists in monitoring the physical and mental health of hospital personnel in order to predict burnout symptoms and to create a sustainable working environment. In education, AI4Work will investigate how to uphold the mental health of both educators and students while fostering a more supportive and enduring educational setting.

Objective: To demonstrate the application of American Association for Thoracic Surgery Quality Gateway (AQG) outcomes models to a Surgeon Case Study of quality assurance in adult cardiac surgery., Methods: The case study includes 6989 cardiac and thoracic aorta operations performed in adults at Cleveland Clinic by a single surgeon between 2001 and 2023. AQG models were used to predict expected probabilities for operative mortality and major morbidity and to compare hospital outcomes, surgery type, risk profile, and individual risk factor levels using virtual (digital) twin causal inference. These models were based on postoperative procedural outcomes after 52,792 cardiac operations performed in 19 hospitals of 3 high-performing hospital systems with overall hospital mortality of 2.0%, analyzed by advanced machine learning for rare events., Results: For individual surgeons, their patients, hospitals, and hospital systems, the Surgeon Case Study demonstrated that AQG provides expected outcomes across the entire spectrum of cardiac surgery, from single-component primary operations to complex multicomponent reoperations. Actionable opportunities for quality improvement based on virtual twins are illustrated for patients, surgeons, hospitals, risk profile groups, operations, and risk factors vis-à-vis other hospitals., Conclusions: Using minimal data collection and models developed using advanced machine learning, this case study shows that probabilities can be generated for operative mortality and major morbidity after virtually all adult cardiac operations. It demonstrates the utility of 21st century causal inference (virtual [digital] twin) tools for assessing quality for surgeons asking "how am I doing?," their patients asking "what are my chances?," and the profession asking "how can we get better?", Competing Interests: Conflict of Interest Statement Drs Blackstone, Ishwaran, and Ms DiPaola are involved in developing the AATS Quality Gateway. All other authors reported no conflicts of interest. The Journal policy requires editors and reviewers to disclose conflicts of interest and to decline handling or reviewing manuscripts for which they may have a conflict of interest. The editors and reviewers of this article have no conflicts of interest., (Copyright © 2024 The American Association for Thoracic Surgery. Published by Elsevier Inc. All rights reserved.)

Biodiversity is a multidimensional concept that can be understood and measured in many different ways. However, the next generation of digital technologies for biodiversity monitoring currently being funded and developed fail to engage its multidimensional and relational aspects. Based on empirical data from interviews, a conference visit, online meetings, webinars, and project reports, this study articulates four digital logics that structure how biodiversity becomes monitored and understood within recent technological developments. The four digital logics illustrate how intensified practices of capturing, connecting, simulating, and computing produce particular techno-scientific formats for creating biodiversity knowledge. While ongoing projects advance technological development in areas of automation, prediction, and the creation of large-scale species databases, their developmental processes structurally limit the future of biodiversity technology. To better address the complex challenges of the global biodiversity crisis, it is crucial to develop digital technologies and practices that can engage with a wider range of perspectives and understandings of relational and multidimensional approaches to biodiversity.

Background: Patient-specific 3D computational fluid dynamics (CFD) simulations have been used previously to identify the impact of injection parameters (e.g. injection location, velocity, etc.) on the particle distribution and the tumor dose during transarterial injection of radioactive microspheres for treatment of hepatocellular carcinoma. However, these simulations are computationally costly, so we aim to evaluate whether these can be reliably simplified., Methods: We identified and applied five simplification strategies (i.e. truncation, steady flow modelling, moderate and severe grid coarsening, and reducing the number of cardiac cycles) to a patient-specific CFD setup. Subsequently, we evaluated whether these strategies can be used to (1) accurately predict the CFD output (i.e. particle distribution and tumor dose) and (2) quantify the sensitivity of the model output to a specific injection parameter (injection flow rate)., Results: For both accuracy and sensitivity purposes, moderate grid coarsening is the most reliable simplification strategy, allowing to predict the tumor dose with only a maximal deviation of 1.4 %, and a similar sensitivity (deviation of 0.7 %). The steady strategy performs the worst, with a maximal deviation in the tumor dose of 20 % and a difference in sensitivity of 10 %., Conclusion: The patient-specific 3D CFD simulations of this study can be reliably simplified by coarsening the grid, decreasing the computational time by roughly 45 %, which works especially well for sensitivity studies., Competing Interests: Declaration of competing interest None declared., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

A transformation is underway in precision and patient-specific medicine. Rapid progress has been enabled by multiple new technologies including induced pluripotent stem cell-derived cardiac myocytes (iPSC-CMs). Here, we delve into these advancements and their future promise, focusing on the efficiency of reprogramming techniques, the fidelity of differentiation into the cardiac lineage, the functional characterization of the resulting cardiac myocytes, and the many applications of in silico models to understand general and patient-specific mechanisms controlling excitation-contraction coupling in health and disease. Furthermore, we explore the current and potential applications of iPSC-CMs in both research and clinical settings, underscoring the far-reaching implications of this rapidly evolving field., (© 2024 The Author(s). The Journal of Physiology published by John Wiley & Sons Ltd on behalf of The Physiological Society.)

Introduction: The Industrial Internet of Things (IIoT) is a technology that connects devices to collect data and conduct in-depth analysis to provide value-added services to industries. The integration of the physical and digital domains is crucial for unlocking the full potential of the IIoT, and digital twins can facilitate this integration by providing a virtual representation of real-world entities., Objectives: By combining digital twins with the IIoT, industries can simulate, predict, and control physical behaviors, enabling them to achieve broader value and support industry 4.0 and 5.0. Constituents of cooperative IIoT domains tend to interact and collaborate during their complicated operations., Methods: To secure such interaction and collaborations, we introduce a blockchain-based cross-domain authentication protocol for IIoT. The blockchain maintains only each domain's dynamic accumulator, which accumulates crucial materials derived from devices, decreasing the overhead. In addition, we use the on-chain accumulator to effectively validate the unlinkable identities of cross-domain IIoT devices., Results: The implementation of the concept reveals the fact that our protocol is efficient and reliable. This efficiency and reliability of our protocol is also substantiated through comparison with state-of-the-art literature. In contrast to related protocols, our protocol exhibits a minimum 22.67% increase in computation cost efficiency and a 16.35% rise in communication cost efficiency., Conclusion: The developed protocol guarantees data transfer security across the domain and thwarts IoT devices from potential physical attacks. Additionally, in order to protect privacy, anonymity and unlinkability are also guaranteed., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Production and hosting by Elsevier B.V.)

This paper presents computer and color vision research focusing on human color perception in VR environments. A VR art gallery with digital twins of original artworks is created for this experiment. In this research, the field of colorimetry and the application of the L*a*b* and RGB color models are applied. The inter-relationships of the two color models are applied to create a color modification of the VR art gallery environment using C# Script procedures. This color-edited VR environment works with a smooth change in color tone in a given time interval. At the same time, a sudden change in the color of the RGB environment is defined in this interval. This experiment aims to record a user's reaction embedded in a VR environment and the effect of color changes on human perception in a VR environment. This research uses lie detector sensors that record the physiological changes of the user embedded in VR. Five sensors are used to record the signal. An experiment on the influence of the user's color perception in a VR environment using lie detector sensors has never been conducted. This research defines the basic methodology for analyzing and evaluating the recorded signals from the lie detector. The presented text thus provides a basis for further research in the field of colors and human color vision in a VR environment and lays an objective basis for use in many scientific and commercial areas.

Digital twins have been gaining an immense interest in various fields over the last decade. Bringing conventional process simulation models into (near) real time are thought to provide valuable insights for operators, decision makers, and stakeholders in many industries. The objective of this paper is to describe two methods for implementing digital twins at water resource recovery facilities and highlight and discuss their differences and preferable use situations, with focus on the automated data transfer from the real process. Case 1 uses a tailor-made infrastructure for automated data transfer between the facility and the digital twin. Case 2 uses edge computing for rapid automated data transfer. The data transfer lag from process to digital twin is low compared to the simulation frequency in both systems. The presented digital twin objectives can be achieved using either of the presented methods. The method of Case 1 is better suited for automatic recalibration of model parameters, although workarounds exist for the method in Case 2. The method of Case 2 is well suited for objectives such as soft sensors due to its integration with the SCADA system and low latency. The objective of the digital twin, and the required latency of the system, should guide the choice of method. PRACTITIONER POINTS: Various methods can be used for automated data transfer between the physical system and a digital twin. Delays in the data transfer differ depending on implementation method. The digital twin objective determines the required simulation frequency. Implementation method should be chosen based on the required simulation frequency., (© 2024 The Author(s). Water Environment Research published by Wiley Periodicals LLC on behalf of Water Environment Federation.)

The bioprocessing industry is undergoing a significant transformation in its approach to quality assurance, shifting from the traditional Quality by Testing (QbT) to Quality by Design (QbD). QbD, a systematic approach to quality in process development, integrates quality into process design and control, guided by regulatory frameworks. This paradigm shift enables increased operational efficiencies, reduced market time, and ensures product consistency. The implementation of QbD is framed around key elements such as defining the Quality Target Product Profile (QTPPs), identifying Critical Quality Attributes (CQAs), developing Design Spaces (DS), establishing Control Strategies (CS), and maintaining continual improvement. The present critical analysis delves into the intricacies of each element, emphasizing their role in ensuring consistent product quality and regulatory compliance. The integration of Industry 4.0 and 5.0 technologies, including Artificial Intelligence (AI), Machine Learning (ML), Internet of Things (IoT), and Digital Twins (DTs), is significantly transforming the bioprocessing industry. These innovations enable real-time data analysis, predictive modelling, and process optimization, which are crucial elements in QbD implementation. Among these, the concept of DTs is notable for its ability to facilitate bi-directional data communication and enable real-time adjustments and therefore optimize processes. DTs, however, face implementation challenges such as system integration, data security, and hardware-software compatibility. These challenges are being addressed through advancements in AI, Virtual Reality/ Augmented Reality (VR/AR), and improved communication technologies. Central to the functioning of DTs is the development and application of various models of differing types - mechanistic, empirical, and hybrid. These models serve as the intellectual backbone of DTs, providing a framework for interpreting and predicting the behaviour of their physical counterparts. The choice and development of these models are vital for the accuracy and efficacy of DTs, enabling them to mirror and predict the real-time dynamics of bioprocessing systems. Complementing these models, advancements in data collection technologies, such as free-floating wireless sensors and spectroscopic sensors, enhance the monitoring and control capabilities of DTs, providing a more comprehensive and nuanced understanding of the bioprocessing environment. This review offers a critical analysis of the prevailing trends in model-based bioprocessing development within the sector., Competing Interests: Declaration of competing interest The authors declare no competing interests., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

In the context of climate change mitigation, calls for methods that can facilitate co-creative practices and processes between different stakeholders in the development of sustainable and climate-neutral urban districts have been increasingly expressed in recent years. This has included calls for collaborative, accessible, transparent and open tools that can facilitate urban development processes and engage different stakeholders in the different phases and stages of an urban district development process towards shared targets on sustainability and emission reduction. In this paper, we present and examine two practical tools, 1) a co-creation model for developing positive energy district (PED) solutions, and 2) a digital twin tool for shared data sharing and collaboration, which were developed and utilized recently in two development projects focusing on Kera district, Espoo, Finland. The Kera district is a current brownfield area, which, since the early 2020s, has been undergoing a transformation from a former industrial and mass logistics area into a future mixed-use urban district with significant emphasis on sustainability and circular economy targets. We utilize actor-network theory (ANT) to explore the human and the non-human actors and their interrelations related to the district's development. Additionally, we seek to understand the networks that emerge within both the co-creation process and the virtual urban digital twin environment and the role these tools have in supporting the formation and facilitation of multi-stakeholder co-creation networks. Finally, we aim to examine the advantages and disadvantages of integrating a regional urban digital twin and the urban co-creation process to enhance sustainability in urban development projects., Competing Interests: No competing interests were disclosed., (Copyright: © 2024 Tartia J and Hämäläinen M.)

Understanding signaling patterns of transformation and controlling cell phenotypes is a challenge of current biology. Here we applied a cell State Transition Assessment and Regulation (cSTAR) approach to a perturbation dataset of single cell phosphoproteomic patterns of multiple breast cancer (BC) and normal breast tissue-derived cell lines. Following a separation of luminal, basal, and normal cell states, we identified signaling nodes within core control networks, delineated causal connections, and determined the primary drivers underlying oncogenic transformation and transitions across distinct BC subtypes. Whereas cell lines within the same BC subtype have different mutational and expression profiles, the architecture of the core network was similar for all luminal BC cells, and mTOR was a main oncogenic driver. In contrast, core networks of basal BC were heterogeneous and segregated into roughly four major subclasses with distinct oncogenic and BC subtype drivers. Likewise, normal breast tissue cells were separated into two different subclasses. Based on the data and quantified network topologies, we derived mechanistic cSTAR models that serve as digital cell twins and allow the deliberate control of cell movements within a Waddington landscape across different cell states. These cSTAR models suggested strategies of normalizing phosphorylation networks of BC cell lines using small molecule inhibitors.

Industry 5.0 has the capacity to surpass the technology -oriented efficiency of Industry 4.0 and advance sustainable development objectives such as prioritizing human needs, ensuring socio-environmental sustainability, and enhancing resilience. Digital twins and simulation technologies improve manufacturing, evaluate products and operations, and predict any potential adverse consequences. With digital twin technology, everything that exists in the physical world will eventually be duplicated in the digital realm. Within the context of Industry 5.0, this study aims to investigate the impact of digital twin technology on the fan manufacturing sector. The proposal for implementing the enabling industry 5.0 application was presented to the chief engineers of eight distinct fan manufacturers. Out of these, five responded positively and their feedback was subsequently followed up on. Three different avenues such as production, supply chain, and testing transparency were proposed for industry 5.0 implementation. The exploration of testing transparency is being undertaken based on a consensual decision. The Web of Things standard that enables digital twin generation in industry 4.0 is implemented to enable the testing transparency. This data was linked with the internal digital twin of fan motor created using ANSYS. This digital twin can predict the lifespan of the motor by analyzing the temperature of the motor housing surface. Toward sustainability and resilience with Industry 4.0 and Industry 5.0 may provide insights into this alignment., Competing Interests: The authors declare that none of the work reported in this study could have been influenced by any known competing financial interests or personal relationships., (© 2024 The Authors.)

This paper concerns the extension of the Heritage Digital Twin Ontology introduced in previous research to describe the reactivity of digital twins used for cultural heritage documentation by including the semantic description of sensors and activators and all of the process of interacting with the real world. After analysing previous work on the use of digital twins in cultural heritage, a summary description of the Heritage Digital Twin Ontology is provided, and the existing applications of digital twins to cultural heritage are overviewed, with references to reviews summarising the large production of scientific contributions on the topic. Then, a novel ontology named the Reactive Digital Twin Ontology is described, in which sensors, activators, and the decision processes are also semantically described, turning the previous synchronic approach to cultural heritage documentation into a diachronic one. Some case studies exemplify this theory.

Digital twin is a new concept that is rapidly gaining recognition especially in the medical field. Indeed, being a virtual representation of real-world entities and processes, a digital twin can be used to accurately represent the patients' disease, clarify the treatment target, and realize personalized and precise therapies. However, despite being a revolutionary concept, the diffusion of digital twins in type 1 diabetes (T1D) is still limited. In this systematic review, we analyzed structure, operating conditions, and characteristics of digital twins being developed for T1D. Our search covered published documents until March 2024: 220 publications were identified, 37 of which were duplicated entries; in addition, 173 publications were removed after inspection of titles, abstracts, and keywords; and finally, 11 publications were fully reviewed, of which 8 were deemed eligible for inclusion. We found that all eight methodologies are not comprehensive multi-scale virtual replicas of the individual with T1D, but they all focus on describing glucose-insulin metabolism, aiming to simulate glucose concentration resultant from therapeutic interventions. In this review, we will compare and analyze different factors characterizing these digital twins, such as operating principles (mathematical model, twinning procedure, validation and assessment) and the key aspects for practical adoption (inclusion of physical activity, data required for twinning, open-source availability). We will conclude the paper listing which, in our opinion, are the current limitations and future directives of digital twins in T1D, hoping that this article can be helpful to researchers working on diabetes technologies to further develop the use of such an important instrument., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

On December 15, 2023, The National Academies of Sciences, Engineering and Medicine (NASEM) released a report entitled: "Foundational Research Gaps and Future Directions for Digital Twins." The ostensible purpose of this report was to bring some structure to the burgeoning field of digital twins by providing a working definition and a series of research challenges that need to be addressed to allow this technology to fulfill its full potential. In the work presented herein we focus on five specific findings from the NASEM Report: 1) definition of a Digital Twin, 2) using "fit-for-purpose" guidance, 3) developing novel approaches to Verification, Validation and Uncertainty Quantification (VVUQ) of Digital Twins, 4) incorporating control as an explicit purpose for a Digital Twin and 5) using a Digital Twin to guide data collection and sensor development, and describe how these findings are addressed through the design specifications for a Critical Illness Digital Twin (CIDT) aimed at curing sepsis.

Digital twins are a relatively new form of digital modeling that has been gaining popularity in recent years. This is in large part due to their ability to update in real time to their physical counterparts and connect across multiple devices. As a result, much interest has been directed towards using digital twins in the healthcare industry. Recent advancements in smart wearable technologies have allowed for the utilization of human digital twins in healthcare. Human digital twins can be generated using biometric data from the patient gathered from wearables. These data can then be used to enhance patient care through a variety of means, such as simulated clinical trials, disease prediction, and monitoring treatment progression remotely. This revolutionary method of patient care is still in its infancy, and as such, there is limited research on using wearables to generate human digital twins for healthcare applications. This paper reviews the literature pertaining to human digital twins, including methods, applications, and challenges. The paper also presents a conceptual method for creating human body digital twins using wearable sensors.

Radiopharmaceutical therapy (RPT) is a rapidly developing field of nuclear medicine, with several RPTs already well established in the treatment of several different types of cancers. However, the current approaches to RPTs often follow a somewhat inflexible "one size fits all" paradigm, where patients are administered the same amount of radioactivity per cycle regardless of their individual characteristics and features. This approach fails to consider inter-patient variations in radiopharmacokinetics, radiation biology, and immunological factors, which can significantly impact treatment outcomes. To address this limitation, we propose the development of theranostic digital twins (TDTs) to personalize RPTs based on actual patient data. Our proposed roadmap outlines the steps needed to create and refine TDTs that can optimize radiation dose to tumors while minimizing toxicity to organs at risk. The TDT models incorporate physiologically-based radiopharmacokinetic (PBRPK) models, which are additionally linked to a radiobiological optimizer and an immunological modulator, taking into account factors that influence RPT response. By using TDT models, we envisage the ability to perform virtual clinical trials, selecting therapies towards improved treatment outcomes while minimizing risks associated with secondary effects. This framework could empower practitioners to ultimately develop tailored RPT solutions for subgroups and individual patients, thus improving the precision, accuracy, and efficacy of treatments while minimizing risks to patients. By incorporating TDT models into RPTs, we can pave the way for a new era of precision medicine in cancer treatment ., Competing Interests: Competing Interests: The authors have declared that no competing interest exists., (© The author(s).)

Digital Twins have emerged as an outstanding opportunity for precision farming, digitally replicating in real-time the functionalities of objects and plants. A virtual replica of the crop, including key agronomic development aspects such as irrigation, optimal fertilization strategies, and pest management, can support decision-making and a step change in farm management, increasing overall sustainability and direct water, fertilizer, and pesticide savings. In this review, Digital Twin technology is critically reviewed and framed in the context of recent advances in precision agriculture and Agriculture 4.0. The review is organized for each step of agricultural lifecycle, edaphic, phytotechnologic, postharvest, and farm infrastructure, with supporting case studies demonstrating direct benefits for agriculture production and supply chain considering both benefits and limitations of such an approach. Challenges and limitations are disclosed regarding the complexity of managing such an amount of data and a multitude of (often) simultaneous operations and supports.

In recent years, the integration of technological advancements and digitalization into healthcare has brought about a remarkable transformation in care delivery and patient management. Among these advancements, the concept of digital twins (DTs) has recently gained attention as a tool with substantial transformative potential in different clinical contexts. DTs are virtual representations of a physical entity (e.g., a patient or an organ) or systems (e.g., hospital wards, including laboratories), continuously updated with real-time data to mirror its real-world counterpart. DTs can be utilized to monitor and customize health care by simulating an individual's health status based on information from wearables, medical devices, diagnostic tests, and electronic health records. In addition, DTs can be used to define personalized treatment plans. In this study, we focused on some possible applications of DTs in laboratory medicine when used with AI and synthetic data obtained by generative AI. The first point discussed how biological variation (BV) application could be tailored to individuals, considering population-derived BV data on laboratory parameters and circadian or ultradian variations. Another application could be enhancing the interpretation of tumor markers in advanced cancer therapy and treatments. Furthermore, DTs applications might derive personalized reference intervals, also considering BV data or they can be used to improve test results interpretation. DT's widespread adoption in healthcare is not imminent, but it is not far off. This technology will likely offer innovative and definitive solutions for dynamically evaluating treatments and more precise diagnoses for personalized medicine., (© 2024 the author(s), published by De Gruyter, Berlin/Boston.)

The emergence of digital twins has ushered in a new era in civil engineering with a focus on achieving sustainable energy supply, real-time sensing, and rapid warning systems. These key development goals mean the arrival of Civil Engineering 4.0.The advent of triboelectric nanogenerators (TENGs) demonstrates the feasibility of energy harvesting and self-powered sensing. This review aims to provide a comprehensive analysis of the fundamental elements comprising civil infrastructure, encompassing various structures such as buildings, pavements, rail tracks, bridges, tunnels, and ports. First, an elaboration is provided on smart engineering structures with digital twins. Following that, the paper examines the impact of using TENG-enabled strategies on smart civil infrastructure through the integration of materials and structures. The various infrastructures provided by TENGs have been analyzed to identify the key research interest. These areas encompass a wide range of civil infrastructure characteristics, including safety, efficiency, energy conservation, and other related themes. The challenges and future perspectives of TENG-enabled smart civil infrastructure are briefly discussed in the final section. In conclusion, it is conceivable that in the near future, there will be a proliferation of smart civil infrastructure accompanied by sustainable and comprehensive smart services., (© 2024 The Authors. Advanced Science published by Wiley‐VCH GmbH.)

This perspective article explores the use of digital twins (DTs) in medicine, highlighting its capacity to simulate risks and personalize treatments while examining the emerging bioethical concerns. Central concerns include power dynamics, exclusion, and misrepresentation. We propose adopting a relational bioethical approach that advocates for a comprehensive assessment of DTs in medicine, extending beyond individual interactions to consider broader structural relations and varying levels of access to power. This can be achieved through two key relational recommendations: acknowledging the impact of uneven relational structures on access to medical care and promoting social justice by evaluating resource allocation. While DTs in medicine offer promising advancements, a relational bioethical lens may provide a nuanced understanding, fostering equitable, inclusive and responsible integration of DTs into medical practice., Competing Interests: Competing InterestsThe authors declare no competing interests., (© National University of Singapore and Springer Nature Singapore Pte Ltd. 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.)

Background: Maintaining machines effectively continues to be a challenge for industrial organisations, which frequently employ reactive or premeditated methods. Recent research has begun to shift its attention towards the application of Predictive Maintenance (PdM) and Digital Twins (DT) principles in order to improve maintenance processes. PdM technologies have the capacity to significantly improve profitability, safety, and sustainability in various industries. Significantly, precise equipment estimation, enabled by robust supervised learning techniques, is critical to the efficacy of PdM in conjunction with DT development. This study underscores the application of PdM and DT, exploring its transformative potential across domains demanding real-time monitoring. Specifically, it delves into emerging fields in healthcare, utilities (smart water management), and agriculture (smart farm), aligning with the latest research frontiers in these areas., Methodology: Employing the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) criteria, this study highlights diverse modeling techniques shaping asset lifetime evaluation within the PdM context from 34 scholarly articles., Results: The study revealed four important findings: various PdM and DT modelling techniques, their diverse approaches, predictive outcomes, and implementation of maintenance management. These findings align with the ongoing exploration of emerging applications in healthcare, utilities (smart water management), and agriculture (smart farm). In addition, it sheds light on the critical functions of PdM and DT, emphasising their extraordinary ability to drive revolutionary change in dynamic industrial challenges. The results highlight these methodologies' flexibility and application across many industries, providing vital insights into their potential to revolutionise asset management and maintenance practice for real-time monitoring., Conclusions: Therefore, this systematic review provides a current and essential resource for academics, practitioners, and policymakers to refine PdM strategies and expand the applicability of DT in diverse industrial sectors., Competing Interests: Kai Huang is an employee of the JiangSu XCMG HanYun Technologies Co., LTD., Xuzhou, China., (© 2024 Abd Wahab et al.)

This study uses a wind turbine case study as a subdomain of Industrial Internet of Things (IIoT) to showcase an architecture for implementing a distributed digital twin in which all important aspects of a predictive maintenance solution in a DT use a fog computing paradigm, and the typical predictive maintenance DT is improved to offer better asset utilization and management through real-time condition monitoring, predictive analytics, and health management of selected components of wind turbines in a wind farm. Digital twin (DT) is a technology that sits at the intersection of Internet of Things, Cloud Computing, and Software Engineering to provide a suitable tool for replicating physical objects in the digital space. This can facilitate the implementation of asset management in manufacturing systems through predictive maintenance solutions leveraged by machine learning (ML). With DTs, a solution architecture can easily use data and software to implement asset management solutions such as condition monitoring and predictive maintenance using acquired sensor data from physical objects and computing capabilities in the digital space. While DT offers a good solution, it is an emerging technology that could be improved with better standards, architectural framework, and implementation methodologies. Researchers in both academia and industry have showcased DT implementations with different levels of success. However, DTs remain limited in standards and architectures that offer efficient predictive maintenance solutions with real-time sensor data and intelligent DT capabilities. An appropriate feedback mechanism is also needed to improve asset management operations.