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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.)

The cyber-agricultural system (CAS) represents an overarching framework of agriculture that leverages recent advances in ubiquitous sensing, artificial intelligence, smart actuators, and scalable cyberinfrastructure (CI) in both breeding and production agriculture. We discuss the recent progress and perspective of the three fundamental components of CAS - sensing, modeling, and actuation - and the emerging concept of agricultural digital twins (DTs). We also discuss how scalable CI is becoming a key enabler of smart agriculture. In this review we shed light on the significance of CAS in revolutionizing crop breeding and production by enhancing efficiency, productivity, sustainability, and resilience to changing climate. Finally, we identify underexplored and promising future directions for CAS research and development., Competing Interests: Declaration of interests The authors declare no conflicts of interest., (Copyright © 2023 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Introduction: The concept of Digital Twins (DTs) translated to drug development and clinical trials describes virtual representations of systems of various complexities, ranging from individual cells to entire humans, and enables in silico simulations and experiments. DTs increase the efficiency of drug discovery and development by digitalizing processes associated with high economic, ethical, or social burden. The impact is multifaceted: DT models sharpen disease understanding, support biomarker discovery and accelerate drug development, thus advancing precision medicine. One way to realize DTs is by generative artificial intelligence (AI), a cutting-edge technology that enables the creation of novel, realistic and complex data with desired properties., Areas Covered: The authors provide a brief introduction to generative AI and describe how it facilitates the modeling of DTs. In addition, they compare existing implementations of generative AI for DTs in drug discovery and clinical trials. Finally, they discuss technical and regulatory challenges that should be addressed before DTs can transform drug discovery and clinical trials., Expert Opinion: The current state of DTs in drug discovery and clinical trials does not exploit the entire power of generative AI yet and is limited to simulation of a small number of characteristics. Nonetheless, generative AI has the potential to transform the field by leveraging recent developments in deep learning and customizing models for the needs of scientists, physicians and patients.

This commentary explores the convergence of precision health and evolving technologies, including the critical role of artificial intelligence (AI) and emerging technologies in infectious diseases (ID) and microbiology. We discuss their disruptive impact on the ID ecosystem and examine the transformative potential of frontier technologies in precision health, public health, and global health when deployed with robust ethical and data governance guardrails in place., Competing Interests: The authors declare no conflict of interest.

Big data and AI have enabled digital simulation for prediction of future health states or behaviors of specific individuals, populations or humans in general. "Digital simulacra" use multimodal datasets to develop computational models that are virtual representations of people or groups, generating predictions of how systems evolve and react to interventions over time. These include digital twins and virtual patients for in silico clinical trials, both of which seek to transform research and health care by speeding innovation and bridging the epistemic gap between population-based research findings and their application to the individual. Nevertheless, digital simulacra mark a major milestone on a trajectory to embrace the epistemic culture of data science and a potential abandonment of medical epistemological concepts of causality and representation. In doing so, "data first" approaches potentially shift moral attention from actual patients and principles, such as equity, to simulated patients and patient data.

Introduction: Artificial intelligence (AI) based tools offer new opportunities for pharmacovigilance (PV) activities. Nevertheless, their contribution to PV needs to be tailored to preserve and strengthen medical and pharmacological expertise in drug safety., Areas Covered: This work aims to describe PV tasks in which the contribution of AI and intelligent automation (IA) tools is required, in the context of a continuous increase of spontaneous reporting cases and regulatory tasks. A narrative review with expert selection of pertinent references was performed through Medline. Two areas were covered, management of spontaneous reporting cases and signal detection., Perspective: The use of AI and IA tools will assist a large spectrum of PV activities, both in public and private PV systems, in particular for tasks of low added value (e.g. initial quality check, verification of essential regulatory information, search for duplicates). Testing, validating, and integrating these tools in the PV routine are the actual challenges for modern PV systems, to guarantee high-quality standards in terms of case management and signal detection.

Computational models are being explored to simulate in silico the efficacy and safety of drug candidates and medical devices. Disease models that are based on patients' profiling data are being produced to represent interactomes of genes or proteins and to infer causality in the pathophysiology, which makes it possible to mimic the impact of drugs on relevant targets. Virtual patients designed from medical records as well as digital twins are generated to simulate specific organs and to predict treatment efficacy at the individual patient level. As the acceptance of digital evidence by regulators grows, predictive artificial intelligence (AI)-based models will support the design of confirmatory trials in humans and will accelerate the development of efficient drugs and medical devices., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

New technologies enable the creation of digital twin systems (DTS) combining continuous data collection from children's home and artificial intelligence (AI)-based recommendations to adapt their care in real time. The objective was to assess whether children and adolescents with asthma would be ready to use such DTS. A mixed-method study was conducted with 104 asthma patients aged 8 to 17 years. The potential advantages and disadvantages associated with AI and the use of DTS were collected in semi-structured interviews. Children were then asked whether they would agree to use a DTS for the daily management of their asthma. The strength of their decision was assessed as well as the factors determining their choice. The main advantages of DTS identified by children were the possibility to be (i) supported in managing their asthma (ii) from home and (iii) in real time. Technical issues and the risk of loss of humanity were the main drawbacks reported. Half of the children (56%) were willing to use a DTS for the daily management of their asthma if it was as effective as current care, and up to 93% if it was more effective. Those with the best computer skills were more likely to choose the DTS, while those who placed a high value on the physician-patient relationship were less likely to do so.   Conclusions: The majority of children were ready to use a DTS for the management of their asthma, particularly if it was more effective than current care. The results of this study support the development of DTS for childhood asthma and the evaluation of their effectiveness in clinical trials. What is Known: • New technologies enable the creation of digital twin systems (DTS) for children with asthma. • Acceptance of these DTSs by children with asthma is unknown. What is New: • Half of the children (56%) were willing to use a DTS for the daily management of their asthma if it was as effective as current care, and up to 93% if it was more effective. •Children identified the ability to be supported from home and in real time as the main benefits of DTS., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

The accelerating transition of traditional industrial processes towards fully automated and intelligent manufacturing is being witnessed in almost all segments. This major adoption of enhanced technology and digitization processes has been originally embraced by the Factories of the Future and Industry 4.0 initiatives. The overall aim is to create smarter, more sustainable, and more resilient future-oriented factories. Unsurprisingly, introducing new production paradigms based on technologies such as machine learning (ML), the Internet of Things (IoT), and robotics does not come at no cost as each newly incorporated technique poses various safety and security challenges. Similarly, the integration required between these techniques to establish a unified and fully interconnected environment contributes to additional threats and risks in the Factories of the Future. Accumulating and analyzing seemingly unrelated activities, occurring simultaneously in different parts of the factory, is essential to establish cyber situational awareness of the investigated environment. Our work contributes to these efforts, in essence by envisioning and implementing the SMS-DT, an integrated platform to simulate and monitor industrial conditions in a digital twin-based architecture. SMS-DT is represented in a three-tier architecture comprising the involved data and control flows: edge, platform, and enterprise tiers. The goal of our platform is to capture, analyze, and correlate a wide range of events being tracked by sensors and systems in various domains of the factory. For this aim, multiple components have been developed on the basis of artificial intelligence to simulate dominant aspects in industries, including network analysis, energy optimization, and worker behavior. A data lake was also used to store collected information, and a set of intelligent services was delivered on the basis of innovative analysis and learning approaches. Finally, the platform was tested in a textile industry environment and integrated with its ERP system. Two misuse cases were simulated to track the factory machines, systems, and people and to assess the role of SMS-DT correlation mechanisms in preventing intentional and unintentional actions. The results of these misuse case simulations showed how the SMS-DT platform can intervene in two domains in the first scenario and three in the second one, resulting in correlating the alerts and reporting them to security operators in the multi-domain intelligent correlation dashboard.

Nondestructive evaluation (NDE) techniques are used in many industries to evaluate the properties of components and inspect for flaws and anomalies in structures without altering the part's integrity or causing damage to the component being tested. This includes monitoring materials' condition (Material State Awareness (MSA)) and health of structures (Structural Health Monitoring (SHM)). NDE techniques are highly valuable tools to help prevent potential losses and hazards arising from the failure of a component while saving time and cost by not compromising its future usage. On the other hand, Artificial Intelligence (AI) and Machine Learning (ML) techniques are useful tools which can help automating data collection and analyses, providing new insights, and potentially improving detection performance in a quick and low effort manner with great cost savings. This paper presents a survey on state of the art AI-ML techniques for NDE and the application of related smart technologies including Machine Vision (MV) and Digital Twins in NDE.

Background: The concept of digital twins has great potential for transforming the existing health care system by making it more personalized. As a convergence of health care, artificial intelligence, and information and communication technologies, personalized health care services that are developed under the concept of digital twins raise a myriad of ethical issues. Although some of the ethical issues are known to researchers working on digital health and personalized medicine, currently, there is no comprehensive review that maps the major ethical risks of digital twins for personalized health care services., Objective: This study aims to fill the research gap by identifying the major ethical risks of digital twins for personalized health care services. We first propose a working definition for digital twins for personalized health care services to facilitate future discussions on the ethical issues related to these emerging digital health services. We then develop a process-oriented ethical map to identify the major ethical risks in each of the different data processing phases., Methods: We resorted to the literature on eHealth, personalized medicine, precision medicine, and information engineering to identify potential issues and developed a process-oriented ethical map to structure the inquiry in a more systematic way. The ethical map allows us to see how each of the major ethical concerns emerges during the process of transforming raw data into valuable information. Developers of a digital twin for personalized health care service may use this map to identify ethical risks during the development stage in a more systematic way and can proactively address them., Results: This paper provides a working definition of digital twins for personalized health care services by identifying 3 features that distinguish the new application from other eHealth services. On the basis of the working definition, this paper further layouts 10 major operational problems and the corresponding ethical risks., Conclusions: It is challenging to address all the major ethical risks that a digital twin for a personalized health care service might encounter proactively without a conceptual map at hand. The process-oriented ethical map we propose here can assist the developers of digital twins for personalized health care services in analyzing ethical risks in a more systematic manner., (©Pei-hua Huang, Ki-hun Kim, Maartje Schermer. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 31.01.2022.)

Over the next 10 years, we are likely to see the convergence of two independent evolutionary paths: one leading to an augmentation of machine capabilities; the other with the augmentation of human capabilities. This convergence will not happen at a specific point in time; instead, it will be the result of progressive overlapping, to the point that it might be difficult to identify a defining moment. The following decade will likely be quite different from the present one. 5G will probably be remembered as a transitional system, artificial intelligence (AI) as a misplaced objective. We are looking forward to a communications fabric created by autonomous systems that will exist both in the physical world as well as in cyberspace, determining a continuum that gives rise to digital reality and where intelligence is an emerging property of the ambient. Hence, the dichotomy between AI and natural intelligence will no longer exist and AI will be considered as a tool for human augmentation and as the glue connecting minds and machines. This article is part of the theme issue 'Towards symbiotic autonomous systems'.

The production of pharmaceuticals, industrial chemicals, and food ingredients from biotechnological processes is a vast and rapidly growing industry. While advances in synthetic biology and metabolic engineering have made it possible to produce thousands of new molecules from cells, few of these molecules have reached the market. The traditional methods of strain and bioprocess development that transform laboratory results to industrial processes are slow and use computers and networks only for data acquisition and storage. Digitalization, machine learning (ML), and artificial intelligence (AI) methods are transforming many fields - how can they be applied to bioprocessing to overcome current bottlenecks? What are the challenges, especially for regulatory issues, in the production of biopharmaceuticals? This chapter begins with a discussion of the current challenges for strain and bioprocess development and then considers how digitalization can be used to approach these tasks in completely new ways. Finally, regulatory considerations are addressed, with the goal of incorporating these issues from the outset as new digitalization methods are created.

Digital twins became of greater interest to researchers and practitioners in supply chain and operations management (SCOM). Literature has addressed the need to understand digital twins in SCOM, mostly focusing on fragmented technological solutions and use cases. We start with an integrative literature review to determine which elements belong to research on digital twins in SCOM. We define the seven major elements of a digital twin in SCOM: technology, people, management, organisation, scope, task, and modelling. We also distinguish five major types of digital twins in SCOM: product, process, organisation, supply chain and network-of-networks. Illustration of a SCOM digital twin is provided using an anyLogistix example. We conclude that digital twins in SCOM are not merely a simulation-based replica of a real object but a complex socio-technical phenomenon involved in continuous human-artificial intelligence interactions. This leads to an understanding of the role of digital twins through the lens of Industry 5.0, reconfigurable and viable supply chains. Researchers and practitioners alike can use our framework to structure the knowledge on SCOM digital twins and consider all seven elements when designing and using digital twins. [ABSTRACT FROM AUTHOR]

High quality, safe, and reliable batteries are essential for widespread adoption of electric vehicles. Current Li-ion battery pack manufacturing processes rely on manual inspections to ensure electric vehicle battery quality. Such manual quality control is prone to errors, increasing the chances of defective batteries. This is likely to increase safety and reliability concerns in the public imagination, slowing down the adoption of electric vehicles. Furthermore, manual inspection is time-consuming and likely to become a bottleneck in scaling up electric vehicle battery production. A potential solution to address this need for fast and accurate inspection of batteries is the use of machine vision and robotics. In this study, we use digital twin design and simulation to develop a battery module inspection system that uses cobots and machine vision to inspect electric vehicle batteries for defects. Our proposed system can automate visual quality checks that are currently being done by human operators. The proposed cobotic system has been simulated and validated for a variety of battery defects to achieve fast and reliable detection. Since a digital twin of the cobotic inspection workcell has been used, the battery inspection system, as designed and validated, is ready for immediate implementation. [ABSTRACT FROM AUTHOR]

The digitalization of agriculture is rapidly changing the way farmers do business. With the integration of advanced technology, farmers are now able to increase efficiency, productivity, and precision in their operations. Digitalization allows for real-time monitoring and management of crops, leading to improved yields and reduced waste. This paper presents a review of some of the use cases that digitalization has made an impact in the automation of open-field and closedfield cultivations by means of collecting data about soils, crop growth, and microclimate, or by contributing to more accurate decisions about water usage and fertilizer application. The objective was to address some of the most recent technological advances that are leading to increased efficiency and sustainability of crop production, reduction in the use of inputs and environmental impacts, and releasing manual workforces from repetitive field tasks. The short discussions included at the end of each case study attempt to highlight the limitations and technological challenges toward successful implementations, as well as to introduce alternative solutions and methods that are rapidly evolving to offer a vast array of benefits for farmers by influencing cost-saving measures. This review concludes that despite the many benefits of digitalization, there are still a number of challenges that need to be overcome, including high costs, reliability, and scalability. Most of the available setups that are currently used for this purpose have been custom designed for specific tasks and are still too expensive to be implemented on commercial scales, while others are still in their early stages of development, making them not reliable or scalable for widespread acceptance and adoption by farmers. By providing a comprehensive understanding of the current state of digitalization in agriculture and its impact on sustainable crop production and food security, this review provides insights for policy-makers, industry stakeholders, and researchers working in this field. [ABSTRACT FROM AUTHOR]

Engineer-to-order manufacturing, characterized by highly customized products and complex workflows, presents unique challenges for warehouse management and operational efficiency. This paper explores the potential of a digital twin as a transformative solution for engineer-to-order environments in manufacturing companies realizing prefabricated building components. This paper outlines a methodology encompassing users' requirements and the design to support the development of a digital twin that integrates Internet of Things devices, Building Information Modeling, and artificial intelligence capabilities. It delves into the specific challenges of outdoor warehouse optimization and worker safety within the context of engineer-to-order manufacturing, and how the digital twin aims to address these issues through data collection, analysis, and visualization. The research is conducted through an in-depth analysis of the warehouse of Focchi S.p.A., a leading manufacturer of high-tech prefabricated building envelopes. Focchi's production processes and stakeholder interactions are investigated, and the paper identifies key user groups and their multiple requirements for warehouse improvement. It also examines the potential of the digital twin to streamline communication, improve decision-making, and enhance safety protocols. While preliminary testing results are not yet available, the paper concludes by underlining the significant opportunities offered by a BIM-, IoT-, and AI-powered digital twin for engineer-to-order manufacturers. This research, developed within the IRIS project, serves as a promising model for integrating digital technologies into complex warehouse environments, paving the way for increased efficiency, safety, and ultimately, a competitive edge in the market of manufacturing companies working in the construction industry. [ABSTRACT FROM AUTHOR]

This article examines the incorporation of the Shopping Assistance Automatic Suggestion (SAAS) model into Virtual Reality (VR) environments in order to improve the online shopping experience. The SAAS model employs sophisticated deep learning methods to offer customized product recommendations, which are conveyed by non-player characters (NPCs) via voice-based interactions. Our goal is to develop an interactive shopping experience that replicates real-life interactions by integrating AI-powered recommendations with immersive VR technology. We gather and standardize data from several open commerce databases, such as Amazon Product and Customer Reviews. The SAAS model, in conjunction with GPT-3, BERT, and T5, undergoes training and testing to evaluate its effectiveness across multiple criteria. The results demonstrate that the SAAS model surpasses other models in delivering contextually aware and pertinent recommendations. The integration process outlines the specific steps involved in capturing, processing, and transforming user interactions in virtual reality (VR) into vocal suggestions provided by non-player characters (NPCs). This strategy improves customization and utilizes the immersive features of virtual reality to effectively engage people. The results of our research establish a higher standard for e-commerce, with the goal of enhancing the user experience of online purchasing by making it more instinctive, engaging, and pleasurable. [ABSTRACT FROM AUTHOR]

Interaction-free usage (IfU) will be one of the quantitatively dominant forms of computer use in the future. In qualitative terms, this form of use will cover a wide range of applications, also software that supports communication and cooperation. Digital twins for cooperation and communication will be employed by individual users to maintain a variety of social networking activities. Generative AI will play a decisive role in this development, autonomously identifying user needs, replacing the predominant form of use through prompting with question-and-answer dialogs. These dialogs will also be used to preconfigure systems for IfU phases. The counterpart to IfU, which will become ever less-frequent, is intervening interaction, when users intervene to explore and adjust the performance of AI-based systems in exceptional situations or to optimize them for future task handling. [ABSTRACT FROM AUTHOR]

Modern Information and Communication Technology (ICT) has been used in safety-critical situations for over twenty years. Rooted in Human-Computer Interaction (HCI) and related disciplines, the field of crisis informatics made considerable efforts to investigate social media use and role patterns in crises, facilitate the collection, processing and refinement of social media data, design and evaluate supportive ICT, and provide cumulative and longitudinal research. This narrative review examines contemporary challenges of human-centred crisis informatics and envision trends for the following decade, including (I) a broadening scope of crisis informatics, (II) the professionalisation of cross-platform collaboration of citizen communities and emergency services, (III) expert interfaces for explainable and multimodal artificial intelligence for user-generated content assessment, (IV) internet of things and mobile apps for bidirectional communication and warnings in disruption-tolerant networks, as well as (V) digital twins and virtual reality for the effective training of multi-agency collaboration in hybrid hazards. [ABSTRACT FROM AUTHOR]

The realm of digital twins is experiencing rapid growth and presents a wealth of opportunities for Industry 4.0. In conjunction with traditional simulation methods, digital twins offer a diverse range of possibilities. However, many existing tools in the domain of open‐source digital twins concentrate on specific use cases and do not provide a versatile framework. In contrast, the open‐source digital twin framework, OpenTwins, aims to provide a versatile framework that can be applied to a wide range of digital twin applications. In this article, we introduce a re‐definition of the original OpenTwins platform that enables the management of custom simulation services and the management of FMI simulation services, which is one of the most widely used simulation standards in the industry and its coexistence with machine learning models, which enables the definition of the next‐gen digital twins. Thanks to this integration, digital twins that reflect reality better can be developed, through hybrid models, where simulation data can feed the scarcity of machine learning data and so forth. As part of this project, a simulation model developed through the hydraulic software Epanet was validated in OpenTwins, in addition to an FMI simulation service. The hydraulic model was implemented and tested in an agricultural use case in collaboration with the University of Córdoba, Spain. A machine learning model has been developed to assess the behavior of an FMI simulation through machine learning. [ABSTRACT FROM AUTHOR]

Bottleneck detection and management plays a significant role in the context of Industry 4.0, wherein process chains have become more intricate. The dynamic nature of process chains shifts the bottleneck location, which requires an integrated methodology capable of identifying current as well as predicting future bottlenecks. The paper proposes a cognitive digital twin (CDT) with a novel explainable artificial intelligence (XAI) model. The proposed CDT is capable of (i) detecting existing bottlenecks, (ii) detecting data anomalies and process chain anomalies (iii) estimating shifting bottlenecks due to anomalies, (iv) predicting near future bottlenecks, and (v) the XAI model supports operational and strategic decision making. The usefulness of proposed CDT is demonstrated and validated experimentally on an industry 4.0 compliant learning factory. The proposed novel CDT effectively addresses the process chain bottlenecks (existing, shifting, and future) while the XAI model enhances transparency and trustworthiness for practical implementation. [ABSTRACT FROM AUTHOR]

In this paper, Artificial Intelligence/Knowledge Representation methods are used for the digital modeling of cultural heritage elements. Accordingly, the new concept of digital cultural heritage twin is presented as composed of a physical component and an immaterial component of the cultural entity. The former concerns the physical aspects, i.e. style, name of the artist, execution time, dimension, etc. The latter represents the emotional and intangible aspects transmitted by the entity, i.e. emotions, thoughts, opinions. In order to digitally model the physical and immaterial components of the twin, the Narrative Knowledge Representation Language has been formally introduced and described. It is particularly suitable for representing the immaterial aspects of the cultural entity, as it is capable of modeling in a simple but rigorous and efficient way complex situations and events, behaviours, attitudes, etc. As an experiment, NKRL has been adopted for representing some of the most relevant intangible items of the visual narrative underlying the hidden painting that lies beneath the Mona Lisa (La Gioconda) image painted by Leonardo Da Vinci on the same poplar panel. Real-time application of the resulting knowledge base opens up novel possibilities for the development of virtual objects, chatbots and expert systems, as well as the definition of semantic search platforms related to cultural heritage. [ABSTRACT FROM AUTHOR]

Background: Electrical Impedance Tomography (EIT), combined with variable ventilation strategies and Artificial Intelligence (AI), is poised to revolutionize critical care by transitioning from reactive to predictive approaches. This integration aims to enhance patient outcomes through personalized interventions and real-time monitoring. Methods: this narrative review explores the principles and applications of EIT, variable ventilation, and AI in critical care. EIT impedance sensing creates dynamic images of internal physiology, aiding the management of conditions like Acute Respiratory Distress Syndrome (ARDS). Variable ventilation mimics natural breathing variability to improve lung function and minimize ventilator-induced lung injury. AI enhances EIT through advanced image reconstruction techniques, neural networks, and digital twin technology, offering more accurate diagnostics and tailored therapeutic interventions. Conclusions: the confluence of EIT, variable ventilation, and AI represents a significant advancement in critical care, enabling a predictive, personalized approach. EIT provides real-time insights into lung function, guiding precise ventilation adjustments and therapeutic interventions. AI integration enhances EIT diagnostic capabilities, facilitating the development of personalized treatment plans. This synergy fosters interdisciplinary collaborations and sets the stage for innovative research, ultimately improving patient outcomes and advancing the future of critical care. [ABSTRACT FROM AUTHOR]

The prediction of manoeuvring performance for safe navigation and effective design of ships increasingly depends on artificial intelligence (AI), mainly digital twin technology. This technology requires a digital model of the physical ship. The hydrodynamic coefficients and parameters of these models are commonly obtained through two experimental methods: the planar motion mechanism (PMM) and the circular motion test (CMT). These methods are time-consuming and expensive, which may not be feasible during the early stages of the design process. This study investigates a cost-effective alternative approach to these methods by implementing a grey box method on ships. For the first of these implementations, a full-scale tanker ship was applied with artificial training data of zigzag manoeuvres. A validation study was carried out by comparing the simulation and free-running model test results of the tanker. For the second of these implementations, a scale model of a car carrier was selected, and several numerical search methods were combined to obtain a more accurate digital model. The 3-degree-of-freedom (DOF) Manoeuvring Modelling Group (MMG) models identified through this combination were validated with simulations and compared with the free-running model test results for various manoeuvres. The contribution of this study lies in the accurate capture of the manoeuvring characteristics of the physical model, which is achieved through the use of the adjustment interval and the combination of various numerical search method of the grey box method. Consequently, the developed model can be used in future studies as a faster decision-making tool for determining the straight-line stability or instability of a ship in the ship design and in predicting the manoeuvring performance of the ship. [ABSTRACT FROM AUTHOR]

Europe's transportation infrastructure including bridges requires optimised maintenance plans and adequate monitoring throughout its life cycle to ensure safety and serviceability. The deployment of new technologies can help this goal, providing numerous advantages compared to existing and established inspection and monitoring methods. This deployment can be facilitated by adequate policies and focused research and innovation, taking also into account the European ambitions for safe, secure, and future-proof transportation infrastructure. Considering the above, the objective of this study is threefold. First, it provides an overview of recent policy initiatives in Europe that facilitate the adoption of new and emerging technologies in bridge inspection and monitoring, as well as maintenance in general. Second, it identifies and reviews R&I projects relevant to bridges, including the budgetary expenditure, from the Horizon 2020 European Research Framework Programme which was recently concluded. Finally, it systematically reviews technologies and solutions developed by the identified projects, assessing also their technology readiness. What emerges is substantial research, which includes solutions and technologies (e.g., use of air or ground drones, artificial intelligence, digital twins) that have the potential to reshape the bridge inspection and monitoring state of play in Europe and beyond. [ABSTRACT FROM AUTHOR]

Buildings significantly contribute to global energy consumption and greenhouse gas emissions. This systematic literature review explores the potential of artificial intelegence (AI) to enhance sustainability throughout a building's lifecycle. The review identifies AI technologies applicable to sustainable building practices, examines their influence, and analyses implementation challenges. The findings reveal AI's capabilities in optimising energy efficiency, enabling predictive maintenance, and aiding in design simulation. Advanced machine learning algorithms facilitate data-driven analysis, while digital twins provide real-time insights for decision-making. The review also identifies barriers to AI adoption, including cost concerns, data security risks, and implementation challenges. While AI offers innovative solutions for energy optimisation and environmentally conscious practices, addressing technical and practical challenges is crucial for its successful integration in sustainable building practices. [ABSTRACT FROM AUTHOR]

In a construction project schedule, delays in delivery are one of the most important problems. Delays can be caused by several project components; however, the issue is amplified when delays occur simultaneously. Classifying delays is relevant in order to allocate responsibility to the parties. In Italy, the delay in the delivery of medium and large-sized works in residential urban centers is about 15% compared to the project forecast. Moreover, the AECO sector's ability to adapt to emerging challenges, such as environmental sustainability and digitization, is limited by the lack of innovation in management methods. The aim of this research is to create a methodology for managing the built and to-be-built environment in a digital way. This will optimize the building process by reducing delays and waste of resources. The methodology will use tools such as digital twin (DT), Building Information Modeling (BIM), Internet of Things (IoT), and Artificial Intelligence (AI) algorithms. The integration of lean construction practices can make the use of these technologies even more efficient, supporting better workflow management by using the BIM environment. The paper presents a methodology that can be applied to various scaling factors and scenarios. It is also useful for construction sites that are already in progress. As highlighted below, this brings significant economic-temporal advantages. [ABSTRACT FROM AUTHOR]

The field of multi-omics has witnessed unprecedented growth, converging multiple scientific disciplines and technological advances. This surge is evidenced by a more than doubling in multi-omics scientific publications within just two years (2022–2023) since its first referenced mention in 2002, as indexed by the National Library of Medicine. This emerging field has demonstrated its capability to provide comprehensive insights into complex biological systems, representing a transformative force in health diagnostics and therapeutic strategies. However, several challenges are evident when merging varied omics data sets and methodologies, interpreting vast data dimensions, streamlining longitudinal sampling and analysis, and addressing the ethical implications of managing sensitive health information. This review evaluates these challenges while spotlighting pivotal milestones: the development of targeted sampling methods, the use of artificial intelligence in formulating health indices, the integration of sophisticated n-of-1 statistical models such as digital twins, and the incorporation of blockchain technology for heightened data security. For multi-omics to truly revolutionize healthcare, it demands rigorous validation, tangible real-world applications, and smooth integration into existing healthcare infrastructures. It is imperative to address ethical dilemmas, paving the way for the realization of a future steered by omics-informed personalized medicine. [ABSTRACT FROM AUTHOR]

Businesses of the 21st century are utilizing technology in developing offerings for the growth of their businesses. From taking a minor role in formulating business decisions, technologies now have a larger stake. Disruptive technologies like artificial intelligence, virtual reality, robotics, blockchain, 3D printing, 5G, Internet‐of‐Things, digital twins and augmented reality are changing how marketing strategies are developed and consumer behaviour is shaped. Our special issue contributes to enhancing the academic and industry‐relevant knowledge of such new‐age technologies and their marketing implications. The papers in this issue focus on the changing consumer behaviour in sectors like healthcare, apparel, footwear to highlight the critical role played by disruptive tech in shaping the modern world marketing and business plans. [ABSTRACT FROM AUTHOR]

Purpose: The digital warehouse management system is an emergence that forms a critical part of the transformation of economic structure in Industry 4.0. In the present business scenario, the warehouse management system encounters a messy layout, poor damage control, unsatisfactory order management, lack of visibility and lack of technological interventions. Digital twin (DT) based warehouse system shows the ontology and knowledge graphs for competitive advantage by consolidating and transferring goods directly from an inbound supplier to an outbound customer on short notice and with no or limited storage. There remains a lack of clarity on how the DT can be implemented successfully in warehouse management. Design/methodology/approach: The current literature remains largely unstructured and scattered due to a lack of a systematic approach to integrating the research implications and analysis. This paper probes the conceptualization of the DT with the help of theoretical analysis using the systematic literature analysis method. Findings: The study explores essential concepts such as interoperability and integrability in implementing DT. Further, it analyzes the role of a supply chain control tower (SCCT) in modern supply chain management. A research framework is proposed for practitioners and academicians by incorporating the opportunities and challenges associated with DT implementation. The research findings are mainly threefold: Conceptualization of DT, Featuring SCCT and Exploration of cross-computer platform interfaces, scalability and maintenance strategies. Originality/value: This study is among the first to analyze and review DT applications in warehouse management. Moreover, the study proposes a theoretical toolbox for the practitioners to successfully implement the DT in warehouse DT-based warehouse management system: A theoretical toolbox for future research and applications. [ABSTRACT FROM AUTHOR]

The rapid progress in the development of automation and artificial intelligence (AI) technologies, such as ChatGPT, represents a step-wise change in human's interactions with technology as part of a broader complex, sociotechnical system. Based on historical parallels to the present moment, such changes are likely to bring forth structural shifts to the nature of work, where near and future technologies will occupy key roles as workers or assistants in sports science and sports medicine multidisciplinary teams (MDTs). This envisioned future may bring enormous benefits, as well as a raft of potential challenges. These challenges include the potential to remove many human roles and allocate them to semi- or fully-autonomous AI. Removing such roles and tasks from humans will make many current jobs and careers untenable, leaving a set of difficult and unrewarding tasks for the humans that remain. Paradoxically, replacing humans with technology increases system complexity and makes them more prone to failure. The automation and AI boom also brings substantial opportunities. Among them are automated sentiment analysis and Digital Twin technologies which may reveal novel insights into athlete health and wellbeing and team tactical patterns, respectively. However, without due consideration of the interactions between humans and technology in the broader system of sport, adverse impacts are likely to be felt. Human and AI teamwork may require new ways of thinking. [ABSTRACT FROM AUTHOR]

Research background: Multi-modal synthetic data fusion and analysis, simulation and modelling technologies, and virtual environmental and location sensors shape the industrial metaverse. Visual digital twins, smart manufacturing and sensory data mining techniques, 3D digital twin simulation modelling and predictive maintenance tools, big data and mobile location analytics, and cloud-connected and spatial computing devices further immersive virtual spaces, decentralized 3D digital worlds, synthetic reality spaces, and the industrial metaverse. Purpose of the article: We aim to show that big data computing and extended cognitive systems, 3D computer vision-based production and cognitive neuro-engineering technologies, and synthetic data interoperability improve artificial intelligence-based digital twin industrial metaverse and hyper-immersive simulated environments. Geolocation data mining and tracking tools, image processing computational and robot motion algorithms, and digital twin and virtual immersive technologies shape the economic and business management of extended reality environments and the industrial metaverse. Methods: Quality tools: AMSTAR, BIBOT, CASP, Catchii, R package and Shiny app citation-chaser, DistillerSR, JBI SUMARI, Litstream, Nested Knowledge, Rayyan, and Systematic Review Accelerator. Search period: April 2024. Search terms: "digital twin industrial metaverse" + "artificial Intelligence of Things systems", "multisensory immersive extended reality technologies", and "algorithmic big data simulation and modelling tools". Selected sources: 114 out of 336. Published research inspected: 2022-2024. PRISMA was the reporting quality assessment tool. Dimensions and VOSviewer were deployed as data visualization tools. Findings & value added: Simulated augmented reality and multi-sensory tracking technologies, explainable artificial intelligence-based decision support and cloud-based robotic cooperation systems, and ambient intelligence and deep learning-based predictive analytics modelling tools are instrumental in augmented reality environments and in the industrial metaverse. The economic and business management of the industrial metaverse necessitates connected enterprise production and big data computing systems, simulation and modelling technologies, and virtual reality-embedded digital twins. [ABSTRACT FROM AUTHOR]

This article delves into the intersection of generative AI and digital twins within drug discovery, exploring their synergistic potential to revolutionize pharmaceutical research and development. Through various instances and examples, we illuminate how generative AI algorithms, capable of simulating vast chemical spaces and predicting molecular properties, are increasingly integrated with digital twins of biological systems to expedite drug discovery. By harnessing the power of computational models and machine learning, researchers can design novel compounds tailored to specific targets, optimize drug candidates, and simulate their behavior within virtual biological environments. This paradigm shift offers unprecedented opportunities for accelerating drug development, reducing costs, and, ultimately, improving patient outcomes. As we navigate this rapidly evolving landscape, collaboration between interdisciplinary teams and continued innovation will be paramount in realizing the promise of generative AI and digital twins in advancing drug discovery. [ABSTRACT FROM AUTHOR]

The integration of Digital Twin technology in small and medium‐sized enterprises (SMEs) has brought forth a transformative paradigm in engineering practices. This paper presents a comprehensive overview of the use of Digital Twin in SMEs and its implementation of advanced digital technologies in an engineering context. The paper highlights key benefits, such as the ability to simulate, analyze, and predict the behavior of products or processes before their physical instantiation. By leveraging advanced modeling and simulation techniques, SMEs can efficiently explore multiple design iterations, identify potential issues, and optimize engineering solutions without costly physical prototyping. Furthermore, the paper discusses integrating other advanced digital technologies, such as the Internet of Things (IoT) and Artificial Intelligence (AI), with Digital Twin to create intelligent and interconnected systems. The implementation of Digital Twin technology in SMEs extends beyond product design to encompass various aspects of the product lifecycle, fostering efficiency, accuracy, and innovation. However, challenges such as data integration, cybersecurity, and skill requirements are addressed. With proper planning and investment, SMEs can unlock the full potential of Digital Twin technology to gain a competitive edge in the dynamic engineering domain. In conclusion, this paper demonstrates that the use of Digital Twin in SMEs and its integration with advanced digital technologies revolutionizes engineering practices. By offering virtual representations of physical assets and processes, Digital Twin enables SMEs to make informed decisions, optimize performance, and drive innovation. As the technology evolves, it becomes an indispensable tool for SMEs seeking to thrive in the ever‐evolving engineering landscape. [ABSTRACT FROM AUTHOR]

Management of metabolic disorders involves continuous monitoring of physiology. The growing use of glucose sensors, insulin pumps, and other wearable devices, as well as smartphone-based decision support apps, generates large amounts of real-time data, making diabetes a data-rich domain conducive to digital twin implementation. Mechanistic models of glucose–insulin dynamics have been developed in diabetes research, providing a solid foundation for developing digital twin representations of individual patients' metabolic processes. Artificial intelligence (AI) will play a role in improving the accuracy of digital twin technologies and enable their adaptation and personalization. Case studies have demonstrated that personalized approaches that leverage digital twin technology hold promise for optimizing the management of diabetes by tailoring interventions to individual patients' needs and characteristics. Digital twin technology is emerging as a transformative paradigm for personalized medicine in the management of chronic conditions. In this article, we explore the concept and key characteristics of a digital twin and its applications in chronic non-communicable metabolic disease management, with a focus on diabetes case studies. We cover various types of digital twin models, including mechanistic models based on ODEs, data-driven ML algorithms, and hybrid modeling strategies that combine the strengths of both approaches. We present successful case studies demonstrating the potential of digital twins in improving glucose outcomes for individuals with T1D and T2D, and discuss the benefits and challenges of translating digital twin research applications to clinical practice. [ABSTRACT FROM AUTHOR]

The main purpose of this study is to provide insight into the trend of AI-BIM integration, which has been studied by scholars around the world. To begin, a systematic review and bibliometric analysis was conducted to investigate English articles published between 2015 and 2022. This paper presents a systematic, scientometric, science mapping analysis through qualitative and quantitative evaluation and co-occurrence methods using VOSviewer, CiteSpace, and Gephi software. Conclusions indicate future research should concentrate on integrating AI and other smart systems with BIM to enhance digitalization and improve outcomes throughout the construction project life cycle. Based on the qualitative and quantitative evaluation of each scope (BIM and AI) and their status quo, this study suggests integrating the following domains with BIM to reduce complexity in the construction industry in the future: robotics, cloud systems, AIOT, digital twins, 4D printing, and block chain. [ABSTRACT FROM AUTHOR]

Building energy system management is critical for resource-saving approaches amid climate change-driven energy transitions. This paper presents a digital twin toolchain leveraging modern technologies such as Building Information Modeling (BIM), Artificial Intelligence (AI), Virtual Reality (VR), and Augmented Reality (AR). The toolchain automates the derivation of georeferenced digital twins during Technical Building Equipment (TBE) commissioning. Using a Scan vs. BIM process, discrepancies between as-planned and as-built TBE are identified, allowing automatic updates to the BIM model. Validation methods ensure both physical and functional aspects of the TBE are accurate. VR and AR facilitate off- and on-site commissioning, enabling immersive visualization and live sensor data access. An evaluation in small and large-scale demonstrators shows the toolchain's scalability and efficiency, with promising results in performance and accuracy. Future work aims to integrate more operational data, enhancing the digital twin's capabilities for building energy system management. [ABSTRACT FROM AUTHOR]

This paper will discuss Data, Information, Knowledge, and Wisdom, which is commonly referred to as DIKW. The DIKW Pyramid Model is a hierarchical model that is often referenced in both academic and practitioner circles. This model will be discussed and shown to be faulty on several levels, including a lack of definitional agreement. A new DIKW framework with systems orientation will be proposed that focuses on what the DIKW elements do in the way humans think, not what they are by definition. Information as a replacement for wasted physical resources in goal-oriented tasks will be a central organizing point. The paper will move the DIKW discussion to the computer-based concept of Digital Twins (DTs) and its augmentation of how we can use DIKW to be more effective and efficient. This will especially be the case as we move toward Intelligent Digital Twins (IDTs) with Artificial Intelligence (AI). [ABSTRACT FROM AUTHOR]

The recent rise in the applications of advanced technologies in the sustainable design and construction of transportation infrastructure demands an appropriate medium for their integration and utilization. The relatively new concept of Civil Integrated Management (CIM) is such a medium; it enhances the development of digital twins for infrastructure and also embodies various practices and tools, including the collection, organization, and data-management techniques of digital data for transportation infrastructure projects. This paper presents a comprehensive analysis of advanced CIM tools and technologies and categorizes its findings into the following research topics: application of advanced surveying methods (Advanced Surveying); geospatial analysis tools for project planning (Geospatial Analysis); multidimensional virtual design models (nD Modeling); Integrated Geospatial and Building Information Modeling (GeoBIM); and transportation infrastructure maintenance and rehabilitation planning (Asset Management). Despite challenges such as modeling complexity, technology investment, and data security, the integration of GIS, BIM, and artificial intelligence within asset-management systems hold the potential to improve infrastructure's structural integrity and long-term performance through automated monitoring, analysis, and predictive maintenance during its lifetime. [ABSTRACT FROM AUTHOR]

While digital twins (DTs) have recently gained prominence as a viable option for creating reliable asset representations, many existing frameworks and architectures in the literature involve the integration of different technologies and paradigms, including the Internet of Things (IoTs), data modeling, and machine learning (ML). This complexity requires the orchestration of these different technologies, often resulting in subsystems and composition frameworks that are difficult to seamlessly align. In this paper, we present a scalable compositional framework designed for the development of a DT-based production management system (PMS) with advanced production monitoring capabilities. The conducted approach used to design the compositional framework utilizes the Factory Design and Improvement (FDI) methodology. Furthermore, the validation of our proposed framework is illustrated through a case study conducted in a phosphate screening station within the context of the mining industry. [ABSTRACT FROM AUTHOR]

The next generation of wireless networks, 6G is being designed with data-intensive applications. One of the key technologies that will enable 6G is blockchain technology. The emergence of blockchain technology and 6G networks has revolutionized service migration. Service migration in 6G networks is a complex process that requires the integration of new technologies, such as artificial intelligence (AI), edge computing, and network slicing. Motivated by these facts, this comprehensive review includes an overview of blockchain and service migration integration in 6G. First, state of art, development frame work and related works were introduced. Then, we used content analysis by WordStat software and bibliographic analysis by VOSviewer to analysis the current status of service migration and blockchain integration in 6G networks. Next, patterns and characteristics, benefits and challenges and potential cases were reviewed. Then, we proposed an architectural blockchain-based model including decentralized architecture, edge computing, network slicing, softwaredefined networking, and 5G-6G interworking in 6G. Finally, we described potential application service migration-based in 6G networks including digital twin (DT), holograms, robot avatar, high density internet of things (IoT), AR and VR in 6G and collected open research and future directions of service migration and blockchain. [ABSTRACT FROM AUTHOR]

This paper is part of an ongoing research study on developing a methodology for the low-cost creation of the Digital Twin of an urban neighborhood for sustainable, transparent, and participatory urban management to enable low-and middle-income economies to meet the UN Sustainable Development Agenda 2030 successfully and timely, in particular SDGs 1, 7, 9, 10, 11, and 12. The methodology includes: (1) the creation of a geospatial data infrastructure by merging Building Information Models (BIMs) and 3D cadastral data that may support a number of applications (i.e., visualization of 3D volumetric legal entities), and (2) the use of Artificial Intelligence (AI) platforms, Machine Learning (ML), and sensors that are interconnected with devices located in the various property units to test and predict future scenarios and support energy efficiency applications. Two modular platforms are created: (1) to interact with the AI sensors for building tracking and management purposes (i.e., alarms, security cameras, control panels, etc.) and (2) to analyze the energy consumption data such as future predictions, anomaly detection, and scenario making. A case study is made for an urban neighborhood in Athens. It includes a dynamic weather simulation and visualization of different seasons and times of day in combination with internal energy consumption. [ABSTRACT FROM AUTHOR]

This paper outlines the main idea and approach of the H2020 ASSISTANT (LeArning and robuSt deciSIon SupporT systems for agile mANufacTuring environments) project. ASSISTANT is aimed at the investigation of AI-based tools for adaptive manufacturing environments, and focuses on the development of a set of digital twins for integration with, management of, and decision support for production planning and control. The ASSISTANT tools are based on the approach of extending generative design, an established methodology for product design, to a broader set of manufacturing decision making processes; and to make use of machine learning, optimisation, and simulation techniques to produce executable models capable of ethical reasoning and data-driven decision making for manufacturing systems. Combining human control and accountable AI, the ASSISTANT toolsets span a wide range of manufacturing processes and time scales, including process planning, production planning, scheduling, and real-time control. They are designed to be adaptable and applicable in a both general and specific manufacturing environments. [ABSTRACT FROM AUTHOR]