ABSTRACT
"With the advance of data science enabling factors such as easy access to scalable memory and computing resources;our growing competence in collecting, storing, and contextualizing data;advances in robotics;[and] the quickly evolving method landscape driven by the open-source community, the benefits of automation and simulation are becoming accessible in the notoriously complicated realm of biopharma manufacturing," says Marcel von der Haar, head of product strategy data analytics at Sartorius. "Plug-and-play" capabilities of automation systems, which enable flexible manufacturing and faster technology transfer, are more important than ever, he says. Walvax Biotech's new COVID-19 mRNA vaccine plant in China is another example of an intelligent and digital plant;it uses Honeywell's batch process control, building and energy management solution systems, and digital twins to monitor assets (5). "Automation brings in the data for machine learning to model the dynamic processes of cell growth and map it against the multiple dimensions provided by advanced sensors," explains Brandl.
ABSTRACT
Digital technologies that could meet these new challenges and aid manufacturing scale-up and speed to market, such as automated digital data collection and augmented and virtual reality (AR/VR) remote collaboration tools, were already available and had been adopted by some, but the new demand spurred greater adoption. "There is a cultural aspect to digitalization because it's a significant investment that results in changes to the operational structure of a facility;it is beneficial when the digitalization comes from the top," explains Yvonne Duckworth, automation engineer and Industry 4.0 subject matter expert at the CRB Group, a life sciences engineering and construction company. Machine sensors and process analytical technology (PAT) instruments can communicate directly with data collection systems using the NoT. Efficient development and tech transfer for mRNA vaccine manufacturing The data analysis and clear communication allowed by digital tools has demonstrated its benefits for process development and technical transfer, making time to market faster.
ABSTRACT
PurposeA digital supply chain (DSC) positively enhances circular economy (CE) practices. However, what factors and conditions lead to the implementation of DSC for transitioning toward CE is not yet clear. Therefore, this study aims at identifying and subsequently analyzing the antecedents of DSC for CE.Design/methodology/approachThe study identifies major antecedents of DSC for CE to achieve sustainability objectives through literature review and expert opinions. In this study, 19 potential antecedents of DSCs for CE are established from the literature and suggestions from industry professionals. A trapezoidal fuzzy Decision-Making Trial and Evaluation Laboratory (DEMATEL) approach is applied quantitatively to investigate the antecedents identified.FindingsConducted in the context of Indian automobile manufacturing industry, the findings of the study reflect that advanced information sharing arrangement, effective government policies for DSC and CE implementation and digitalizing the supply chains are the top three potential antecedents of DSC for a CE.Originality/valueIn the existing literature, few studies are specific to investigating the DSC and CE paradigm. The present study will help organizations develop a practical and integrated strategic approach that will foster DSC through improved knowledge of CE.
ABSTRACT
COVID -19 impedes construction productivity, increases costs and delays the project schedule, disrupts health and safety regulations, and reduces profit margins. To flatten the COVID -19 curve and continue operations amid the pandemic, construction companies are digitising construction activities, processes and procedures using technology. This article reports on a study examining the use of construction technologies by Nigerian surveyors through a questionnaire. The questionnaires were sent to more than 300 surveying firms based on snowballing technique. Association rule mining (ARM) was used to model the correlations between the different construction technologies. A total of 91 association rules were identified using 6 measurement matrices. The first 30 rules are presented in this study. This study contributes to the body of knowledge on the sustainability and post-pandemic redesign of surveying practise in terms of repositioning service delivery. The findings are of interest to solution and technology providers to meet market demand. From an ontological perspective, the key findings of this study can be applied to surveying practise in and outside Nigeria and to other professionals in the construction sector. The implication is that construction technologies in the Nigerian construction sector are still in their infancy.
ABSTRACT
The COVID-19 pandemic led to an acceleration of digitalisation in healthcare institutions, not only in the medical field but also within non-medical, which includes facility management (FM). FM organisations are increasingly confronted with the need to digitally transform their operations and to implement new digital technologies. This paper aims at providing scholars and professionals with an overview of the various digital technologies and systems that are relevant in shaping the digital transformation. An integrative literature review has been chosen, as it provides a systematic approach to map, collate and report on key findings and concepts from the literature for researchers and practitioners. Overall, 33 articles were systematically reviewed. 22 different digital technologies and systems were identified in the literature and were added to so-called technology clusters. From all the described technologies, Building Information Modelling (BIM) is most prominently cited. Furthermore, Internet of Things (IoT), Artificial Intelligence (AI) and Machine Learning (ML), Digital Twins (DT), and Blockchain technologies are commonly found. Additional technologies and systems mentioned in the literature, though not further detailed, were also added within a separate cluster. This study also discusses the implications for the digital transformation which is important when introducing novel digital technologies in healthcare organisations. It is argued that FM in healthcare needs to focus on integrating technologies, both at a technological level, and particularly at an organisational and interorganisational level.
ABSTRACT
The ocean holds abundant resources, but the utilization of those resources for the marine economy presents a complex and dynamic industrial situation. Exploring sustainable development in this industry is of practical value, as it involves the rational use of marine resources while protecting the environment. This study provides an innovative review of the current application status of Digital Twins Technology (DTT) in various sectors of the marine industry, including the ship-building industry (SBI), Offshore Oil and Gas Industry, marine fishery, and marine energy industry. The findings reveal that DTT offers robust support for full life cycle management (LCM) in SBI, including digital design, intelligent processing, operation, and error management. Furthermore, this work delves into the challenges and prospects of DTT application in the marine industry, aiming to provide reference and direction for intelligent systems in the industry and guide the rational development and utilization of marine resources in the future.
ABSTRACT
The popularity of the online teaching model increased during the COVID-19, and virtual reality online education is now firmly established as a future trend in educational growth. Human–computer interaction and collaboration between virtual models and physical entities, as well as virtual multi-sensory cognition, have become the focus of research in the field of online education. In this paper, we analyze the mapping form of teaching information and cue information on users' cognition through an experimental system and investigate the effects of the presentation form of online virtual teaching information, the length of the material, users' memory of the information, and the presentation form of information cues on users' cognitive performance. The experimental results show that different instructional information and cue presentation designs have significant effects on users' learning performance, with relatively longer instructional content being more effective and users being more likely to mechanically remember the learning materials. By studying the impact of multi-sensory information presentation on users' cognition, the output design of instructional information can be optimized, cognitive resources can be reasonably allocated, and learning effectiveness can be ensured, which is of great significance for virtual education research in digital twins.
ABSTRACT
Due to the outbreak of COVID-19, increasing attention has been paid to designing a cold chain logistics mechanism to ensure the quality of vaccine delivery. In this study, a cold chain digital twins-based risk analysis model is constructed to handle and monitor the vaccine delivery process with a high level of reliability and traceability. The model integrates the Internet of Things (IoT) and digital twins to acquire data on environmental conditions and shipment movements and connect physical cold chain logistics to the digital world. Through the simulation of cold chain logistics in a virtual environment, the risk levels relating to physical operations at a certain forecast horizon can be predicted beforehand, to prevent a 'broken' cold chain. The result of this investigation will reshape the cold chain in the digital age, benefit society in terms of sustainability and environmental impact, and hence contribute to the development of cold chain logistics in Hong Kong. © 2023 IEEE.
ABSTRACT
The deadly coronavirus disease (COVID-19) has highlighted the importance of remote health monitoring (RHM). The digital twins (DTs) paradigm enables RHM by creating a virtual replica that receives data from the physical asset, representing its real-world behavior. However, DTs use passive internet of things (IoT) sensors, which limit their potential to a specific location or entity. This problem can be addressed by using the internet of robotic things (IoRT), which combines robotics and IoT, allowing the robotic things (RTs) to navigate in a particular environment and connect to IoT devices in the vicinity. Implementing DTs in IoRT, creates a virtual replica (virtual twin) that receives real-time data from the physical RT (physical twin) to mirror its status. However, DTs require a user interface for real-time interaction and visualization. Virtual reality (VR) can be used as an interface due to its natural ability to visualize and interact with DTs. This research proposes a real-time system for RHM of COVID-19 patients using the DTs-based IoRT and VR-based user interface. It also presents and evaluates robot navigation performance, which is vital for remote monitoring. The virtual twin (VT) operates the physical twin (PT) in the real environment (RE), which collects data from the patient-mounted sensors and transmits it to the control service to visualize in VR for medical examination. The system prevents direct interaction of medical staff with contaminated patients, protecting them from infection and stress. The experimental results verify the monitoring data quality (accuracy, completeness, timeliness) and high accuracy of PT’s navigation. Author
ABSTRACT
The production of messenger ribonucleic acid (mRNA) and other biologics is performed primarily in batch mode. This results in larger equipment, cleaning/sterilization volumes, and dead times compared to any continuous approach. Consequently, production throughput is lower and capital costs are relatively high. Switching to continuous production thus reduces the production footprint and also lowers the cost of goods (COG). During process development, from the provision of clinical trial samples to the production plant, different plant sizes are usually required, operating at different operating parameters. To speed up this step, it would be optimal if only one plant with the same equipment and piping could be used for all sizes. In this study, an efficient solution to this old challenge in biologics manufacturing is demonstrated, namely the qualification and validation of a plant setup for clinical trial doses of about 1000 doses and a production scale-up of about 10 million doses. Using the current example of the Comirnaty BNT162b2 mRNA vaccine, the cost-intensive in vitro transcription was first optimized in batch so that a yield of 12 g/L mRNA was achieved, and then successfully transferred to continuous production in the segmented plug flow reactor with subsequent purification using ultra- and diafiltration, which enables the recycling of costly reactants. To realize automated process control as well as real-time product release, the use of appropriate process analytical technology is essential. This will also be used to efficiently capture the product slug so that no product loss occurs and contamination from the fill-up phase is <1%. Further work will focus on real-time release testing during a continuous operating campaign under autonomous operational control. Such efforts will enable direct industrialization in collaboration with appropriate industry partners, their regulatory affairs, and quality assurance. A production scale-operation could be directly supported and managed by data-driven decisions. © 2023 by the authors.
ABSTRACT
Advanced mathematical and deep learning (DL) algorithms have recently played a crucial role in diagnosing medical parameters and diseases. One of these areas that need to be more focused on is dentistry. This is why creating digital twins of dental issues in the metaverse is a practical and effective technique to benefit from the immersive characteristics of this technology and adapt the real world of dentistry to the virtual world. These technologies can create virtual facilities and environments for patients, physicians, and researchers to access a variety of medical services. Experiencing an immersive interaction between doctors and patients can be another considerable advantage of these technologies, which can dramatically improve the efficiency of the healthcare system. In addition, offering these amenities through a blockchain system enhances reliability, safety, openness, and the ability to trace data exchange. It also brings about cost savings through improved efficiencies. In this paper, a digital twin of cervical vertebral maturation (CVM), which is a critical factor in a wide range of dental surgery, within a blockchain-based metaverse platform is designed and implemented. A DL method has been used to create an automated diagnosis process for the upcoming CVM images in the proposed platform. This method includes MobileNetV2, a mobile architecture that improves the performance of mobile models in multiple tasks and benchmarks. The proposed technique of digital twinning is simple, fast, and suitable for physicians and medical specialists, as well as for adapting to the Internet of Medical Things (IoMT) due to its low latency and computing costs. One of the important contributions of the current study is to use of DL-based computer vision as a real-time measurement method so that the proposed digital twin does not require additional sensors. Furthermore, a comprehensive conceptual framework for creating digital twins of CVM based on MobileNetV2 within a blockchain ecosystem has been designed and implemented, showing the applicability and suitability of the introduced approach. The high performance of the proposed model on a collected small dataset demonstrates that low-cost deep learning can be used for diagnosis, anomaly detection, better design, and many more applications of the upcoming digital representations. In addition, this study shows how digital twins can be performed and developed for dental issues with the lowest hardware infrastructures, reducing the costs of diagnosis and treatment for patients.
ABSTRACT
Mathematical modeling has been fundamental to achieving near real-time accurate forecasts of the spread of COVID-19. Similarly, the design of non-pharmaceutical interventions has played a key role in the application of policies to contain the spread. However, there is less work done regarding quantitative approaches to characterize the impact of each intervention, which can greatly vary depending on the culture, region, and specific circumstances of the population under consideration. In this work, we develop a high-resolution, data-driven agent-based model of the spread of COVID-19 among the population in five Spanish cities. These populations synthesize multiple data sources that summarize the main interaction environments leading to potential contacts. We simulate the spreading of COVID-19 in these cities and study the effect of several non-pharmaceutical interventions. We illustrate the potential of our approach through a case study and derive the impact of the most relevant interventions through scenarios where they are suppressed. Our framework constitutes a first tool to simulate different intervention scenarios for decision-making.
Subject(s)
COVID-19 , Humans , COVID-19/epidemiology , COVID-19/prevention & control , SARS-CoV-2 , Cities , Spain/epidemiology , Models, TheoreticalABSTRACT
This study is aimed to explore the anti-epidemic effect of artificial intelligence (AI) algorithms such as digital twins on the COVID-2019 (novel coronavirus disease 2019), so that the information security and prediction accuracy of epidemic prevention and control (P & C) in smart cities can be further improved. It addresses the problems in the current public affairs governance strategy for the outbreak of the COVID-2019 epidemic, and uses digital twins technology to map the epidemic P & C situation in the real space to the virtual space. Then, the blockchain technology and deep learning algorithms are introduced to construct a digital twins model of the COVID-2019 epidemic (the COVID-DT model) based on blockchain combined with BiLSTM (Bi-directional Long Short-Term Memory). In addition, performance of the constructed COVID-DT model is analyzed through simulation. Analysis of network data security transmission performance reveals that the constructed COVID-DT model shows a lower average delay, its data message delivery rate (DMDR) is basically stable at 80%, and the data message disclosure rate (DMDCR) is basically stable at about 10%. The analysis on network communication cost suggests that the cost of this study does not exceed 700 bytes, and the prediction error does not exceed 10%. Therefore, the COVID-DT model constructed shows high network security performance while ensuring low latency performance, enabling more efficient and accurate interaction of information, which can provide experimental basis for information security and development trends of epidemic P & C in smart cities. © 2022
ABSTRACT
The Metaverse allows the integration of physical and digital versions of users, processes, and environments where entities communicate, transact, and socialize. With the shift towards Extended Reality (XR) technologies, the Metaverse is envisioned to support a wide range of applicative verticals. It will support a seamless mix of physical and virtual worlds (realities) and, thus, will be a game changer for the Future Internet, built on the Semantic Web framework. The Metaverse will be ably assisted by the convergence of emerging wireless communication networks (such as Fifth-Generation and Beyond networks) or Sixth-Generation (6G) networks, Blockchain (BC), Web 3.0, Artificial Intelligence (AI), and Non-Fungible Tokens (NFTs). It has the potential for convergence in diverse industrial applications such as digital twins, telehealth care, connected vehicles, virtual education, social networks, and financial applications. Recent studies on the Metaverse have focused on explaining its key components, but a systematic study of the Metaverse in terms of industrial applications has not yet been performed. Owing to this gap, this survey presents the salient features and assistive Metaverse technologies. We discuss a high-level and generic Metaverse framework for modern industrial cyberspace and discuss the potential challenges and future directions of the Metaverse's realization. A case study on Metaverse-assisted Real Estate Management (REM) is presented, where the Metaverse governs a Buyer–Broker–Seller (BBS) architecture for land registrations. We discuss the performance evaluation of the current land registration ecosystem in terms of cost evaluation, trust probability, and mining cost on the BC network. The obtained results show the viability of the Metaverse in REM setups.
ABSTRACT
Over the past 50 years, although categorized as the "Information Age" or "Digital Age," the vast amounts of digitized data have been sorely underutilized. Only recently, in response to the COVID-19 pandemic, efforts have accelerated to harness these data using blockchain technology as it pertains to healthcare. Today, through the blockchain infrastructure and its tokenization applications, we are able to leverage healthcare data effectively into more efficient business processes. In addition, we can secure better patient engagement and outcomes, while generating new revenue streams for an array of healthcare stakeholders. It is in the application of blockchain technology to compile these stockpiled data into new, compliant business models that we can reap the full potential of the blockchain. Here are predictions by members of the BHTY editorial board members on how we might further advance the role of blockchain in healthcare in 2023.
ABSTRACT
The purposes are to explore the effect of Digital Twins (DTs) in Unmanned Aerial Vehicles (UAVs) on providing medical resources quickly and accurately during COVID-19 prevention and control. The feasibility of UAV DTs during COVID-19 prevention and control is analyzed. Deep Learning (DL) algorithms are introduced. A UAV DTs information forecasting model is constructed based on improved AlexNet, whose performance is analyzed through simulation experiments. As end-users and task proportion increase, the proposed model can provide smaller transmission delays, lesser energy consumption in throughput demand, shorter task completion time, and higher resource utilization rate under reduced transmission power than other state-of-art models. Regarding forecasting accuracy, the proposed model can provide smaller errors and better accuracy in Signal-to-Noise Ratio (SNR), bit quantizer, number of pilots, pilot pollution coefficient, and number of different antennas. Specifically, its forecasting accuracy reaches 95.58% and forecasting velocity stabilizes at about 35 Frames-Per-Second (FPS). Hence, the proposed model has stronger robustness, making more accurate forecasts while minimizing the data transmission errors. The research results can reference the precise input of medical resources for COVID-19 prevention and control.
ABSTRACT
Vehicular Ad-Hoc Networks (VANETs), as the crucial support of Intelligent Transportation Systems (ITS), have received great attention in recent years. With the rapid development of VANETs, various services have generated a great deal of data that can be used for transportation planning and safe driving. Especially, with the advent of Coronavirus Disease 2019 (COVID-19), the transportation system has been impacted, thus novel modes of transportation planning and intelligent applications are necessary. Digital twins can provide powerful support for artificial intelligence applications in Transportation Big Data (TBD). The features of VANETs are varying, which arises the main challenge of digital twins applying in TBD. Network traffic prediction, as part of digital twins, is useful for network management and security in VANETs, such as network planning and anomaly detection. This paper proposes a network traffic prediction algorithm aiming at time-varying traffic flows with a large number of fluctuations. This algorithm combines Deep Q-Learning (DQN) and Generative Adversarial Networks (GAN) for network traffic feature extraction. DQN is leveraged to carry out network traffic prediction, in which GAN is involved to represent Q-network. Meanwhile, the generative network can increase the number of samples to improve the prediction error. We evaluate the performance of our method by implementing it on three real network traffic data sets. Finally, we compare the two state-of-the-art competing methods with our method. IEEE
ABSTRACT
With the rapid development of information technology and the spread of Corona Virus Disease 2019 (COVID-19), the government and urban managers are looking for ways to use technology to make the city smarter and safer. Intelligent transportation can play a very important role in the joint prevention. This work expects to explore the building information modeling (BIM) big data (BD) processing method of digital twins (DTs) of Smart City, thus speeding up the construction of Smart City and improve the accuracy of data processing. During construction, DTs build the same digital copy of the smart city. On this basis, BIM designs the building's keel and structure, optimizing various resources and configurations of the building. Regarding the fast data growth in smart cities, a complex data fusion and efficient learning algorithm, namely Multi-Graphics Processing Unit (GPU), is proposed to process the multi-dimensional and complex BD based on the compositive rough set model. The Bayesian network solves the multi-label classification. Each label is regarded as a Bayesian network node. Then, the structural learning approach is adopted to learn the label Bayesian network's structure from data. On the P53-old and the P53-new datasets, the running time of Multi-GPU decreases as the number of GPUs increases, approaching the ideal linear speedup ratio. With the continuous increase of K value, the deterministic information input into the tag BN will be reduced, thus reducing the classification accuracy. When K = 3, MLBN can provide the best data analysis performance. On genbase dataset, the accuracy of MLBN is 0.982 +/- 0.013. Through experiments, the BIM BD processing algorithm based on Bayesian Network Structural Learning (BNSL) helps decision-makers use complex data in smart cities efficiently.
ABSTRACT
Les auteurs pensent que le métavers est une technologie d'infrastructure de changement de paradigme qui pourrait rendre 60% des universités non pertinentes dans les 20 prochaines années si elles ne s'éloignent pas du modèle des méta-études. Le modèle des méta-études est un processus d'archivage, largement basé sur le papier, qui consiste à "enseigner" ce qui a été écrit par d'autres personnes. Ce modèle est orienté vers le passé et soutenu par l'impératif "publier ou périr". Bien que la pandémie de COVID-19 ait forcé les universités à adopter des technologies qui font partie du Métavers, les universités ont surtout utilisé ces technologies pour perpétuer le statu quo. Les universités doivent changer leurs pratiques et devenir plus flexibles, en développant le design thinking, le questionnement et l'esprit critique, tant pour les étudiants que pour elles-mêmes en tant qu'institutions. Si elles ne le font pas, des entités commerciales telles que Meta et Microsoftdécideront de l'enseignement dispensé dans le métavers. Si cela se produit, les étudiants iront vers ces entités commerciales pour apprendre, que les universités soient présentes ou non. Il est donc important que les universités changent leurs paradigmes et adoptent cette technologie. Ce faisant, elles découvriront comment le métavers peut être utilisé efficacement dans l'éducation pour favoriser les principes et les valeurs permettant de construire des connaissances et de cimenter des pratiques éthiques et pérennes.Alternate :The authors believe that the metaverse is a paradigm shift infrastructure technology that may make 60% of universities irrelevant in the next 20 years if they do not move away from the metastudies model. The metastudies model is an archival, largely paper-based process of "teaching" what other people have written about. The metastudies model is focused on the past and supported by the publish-or-perish imperative. Although the COVID-19 pandemic has forced universities to adopt technologies that form a part of the metaverse, universities have mostly used these technologies to perpetuate the status quo. Universities need to change their practices and become more agile, adopting design thinking, inquiring minds, and critical thinking both for students and for themselves as institutions. If they do not, commercial entities such as Meta and Microsoft will decide what education takes place in the metaverse. If that happens, students will go to those commercial entities to learn, whether universities are present or not. It is therefore important that universities change their paradigms and engage with this technology. By doing so, they will discover how metaverse can be used effectively in education to foster principles and values for building knowledge and cementing practices that are ethical and sustainable.
ABSTRACT
Different kind of smart healthcare ecosystems have been adopted in the last few years usually based on the continuous monitoring of people as well as other physical entities such as buildings or devices. The diverse nature and origins of the great amount of data that those novel smart healthcare ecosystems must process poses additional data management issues that restrict and difficult their design and construction. In order to improve data management in smart healthcare ecosystems, a data fabric architecture-like process for data lifecycle management has been obtained from the analysis of different architectural proposals intended for being used for different types of systems and contexts. This process integrates aspects of Digital Twins (DT) to tackle with the data contextualization problems that characterizes data fabric architectures. Based on the proposed approach, a prototype of a novel smart healthcare Internet of Things (IoT)-based ecosystem to prevent the spreading of the virus in a real Spanish nursing home has been developed. The evaluation of the prototype has been carried out following a specific novel IoT-based systems evaluation methodology combined with ISO software quality standards that determined that the system is reliable and efficient in performance. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.