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Digital Twin is becoming an increasingly powerful resource in the field of building production, replacing traditional processes in the Architecture, Engineering, Construction and Operations sector. This study is concerned with the development of a DT, enabled by Building Information Modeling, artificial intelligence, machine learning, and the Internet of Things to implement space management strategies. It proposes an application case for the Lazio Region headquarters, which has partly adopted smart working typology post-COVID-19. The aim is to create an accurate digital replica of the building based on BIM, integrated with real-time data. This will help to improve the use of space, the management of resources, and the quality of services provided to the community. It also improves energy efficiency, reducing consumption by 530.40 MWh per year and reducing greenhouse gas emissions by 641.32 tons of CO2 per year. The research proposes a holistic framework for the implementation of innovative solutions in the context of public infrastructure space management through the use of digital technology, facilitating the promotion of efficiency and sustainability in decision-making and operational processes through the application of a digital methodology. [ABSTRACT FROM AUTHOR]

In recent years, the detection performance of SAR-GMTI (synthetic aperture radar-ground moving target indication) algorithm based on deep learning has always been limited by insufficient measured data due to the heavy operation complexity and high cost of real SAR systems. To solve this problem, this paper proposes an overall DT-based implementation framework for SAR ground moving target intelligent detection tasks. In particular, by virtue of a SAR imaging algorithm, a high-fidelity twin replica of SAR moving targets is established in digital space through parameter traversal based on the prior target characteristics of the obtained measured datasets. Then, the constructed SAR twin datasets is fed into the neural network model to train an intelligent detector by fully learning features of the moving targets and preset the SAR scene in the twin space, which can realize the robust detection of ground moving targets in related practical scenarios with no need for multiple and complex field experiments. Moreover, the effectiveness of the proposed framework is verified on the MiniSAR measured system, and a comparison with traditional CFAR detection method is given simultaneously. [ABSTRACT FROM AUTHOR]

This research leverages the potential of digital twin technology to present a novel method of predictive maintenance for 6G communication networks. With the increasing need for 6G networks to be consistently reliable and always available, this essay emphasises the need for more advanced maintenance techniques. The seamless integration of digital twin technology makes it possible to monitor, analyse, and simulate the 6G network in real time. This innovation makes it easier to use artificial intelligence to fine-tune maintenance schedules and anticipate maintenance requirements in advance. The paper fervently promotes the use of digital twin technology as a vital instrument for raising the bar for maintenance requirements in the context of 6G networks. [ABSTRACT FROM AUTHOR]

This work aims to conduct an analysis to find opportunities for the implementation of software incorporating the concept of digital twins for foundry work. Examples of implementations and their impact on the work of enterprises are presented, as is a definition and history of the concept of a digital twin. The outcome of this work is the implementation of software that involves a digital copy of the author's device, created by the "Łukasiewicz" Research Network at the Krakow Institute of Technology. The research problem of this scientific work is to reduce the number of necessary physical tests on real objects in order to find a solution that saves time and energy when testing the thermal expansion of known and new metal alloys. This will be achieved by predicting the behavior of the sample in a digital environment and avoiding causing it to break in reality. Until now, after an interruption, the device often continued to operate and collect data even though no current was flowing through the material, which could be described as inefficient testing. The expected result will be based on the information and decisions obtained by predicting values with the help of a recurrent neural network. Ultimately, it is intended to predict the condition of the sample after a set period of time. Thanks to this, a decision will be made, based on which the twin will know whether it should automatically end its work, disconnect the power or call the operator for the necessary interaction with the device. The described software will help the operator of a real machine, for example, to operate a larger number of workstations at the same time, without devoting all their attention to a process that may last even for hours. Additionally, it will be possible to start work on selecting the chemical composition of the next material sample and plan its testing in advance. The machine learning handles model learning and value prediction with the help of artificial neural networks that were created in Python. The application uses historical test data, additionally retrieves current information, presents it to the user in a clear modern form and runs the provided scripts. Based on these, it decides on the further operation of the actual device. [ABSTRACT FROM AUTHOR]

Buildings consume a significant amount of energy throughout their lifecycle; Thus, sustainable energy management is crucial for all buildings, and controlling energy consumption has become increasingly important for achieving sustainable construction. Digital twin (DT) technology, which lies at the core of Industry 4.0, has gained widespread adoption in various fields, including building energy analysis. With the ability to monitor, optimize, and predict building energy consumption in real time. DT technology has enabled sustainable building energy management and cost reduction. This paper provides a comprehensive review of the development and application of DT technology in building energy. Specifically, it discusses the background of building information modeling (BIM) and DT technology and their application in energy optimization in buildings. Additionally, this article reviews the application of DT technology in building energy management, indoor environmental monitoring, and building energy efficiency evaluation. It also examines the benefits and challenges of implementing DT technology in building energy analysis and highlights recent case studies. Furthermore, this review emphasizes emerging trends and opportunities for future research, including integrating machine learning techniques with DT technology. The use of DT technology in the energy sector is gaining momentum as efforts to optimize energy efficiency and reduce carbon emissions continue. The advancement of building energy analysis and machine learning technologies is expected to enhance prediction accuracy, optimize energy efficiency, and improve management processes. These advancements have become the focal point of current literature and have the potential to facilitate the transition to clean energy, ultimately achieving sustainable development goals. [ABSTRACT FROM AUTHOR]

The rapid development of digital technologies and solutions is disrupting the energy sector. In this regard, digitalization is a facilitator and enabler for integrating renewable energies, management and operation. Among these, advanced monitoring techniques and artificial intelligence may be applied in solar PV plants to improve their operation and efficiency and detect potential malfunctions at an early stage. This paper proposes a Digital Twin DT concept, mainly focused on O&M, to obtain more information about the system by using several artificial intelligence boxes. Furthermore, it includes the development of several machine learning (ML) algorithms capable of reproducing the expected behavior of the solar PV plant and detecting the malfunctioning of different components. In this regard, this allows for reducing downtime and optimizing asset management. In this paper, different ML techniques are used and compared to optimize the selected methods for enhanced response. The paper presents all stages of the developed Digital Twin, including ML model development with an accuracy of 98.3% of the whole DT, and finally, a communication and visualization platform. The different responses and comparisons have been made using a model based on MATLAB/Simulink using different cases and system conditions. [ABSTRACT FROM AUTHOR]

As companies are trying to build more resilient supply chains using digital twins created by smart manufacturing technologies, it is imperative that senior executives and technology providers understand the crucial role of process simulation and AI in quantifying the uncertainties of these complex systems. The resulting digital twins enable users to replay history, gain predictive visibility into the future, and identify corrective actions to optimize future performance. In this article, we define process digital twins and their four foundational elements. We discuss how key digital twin functions and enabling AI and simulation technologies integrate to describe, predict, and optimize supply chains for Industry 4.0 implementations. [ABSTRACT FROM AUTHOR]

• Evaluating the impact of a cyber-vulnerability on device-level resilience of solar-based networks. • Designing a structure for assessing device resiliency using controller code verification in digital twin environment. • Determining the effect on microgrid resilience applying a new machine learning based cyber-physical resiliency metric (CPRM). • Adjusting control actions to improved situational awareness of solar-based networks. Cyber-attacks (CAs) on electrical networks in presents of renewable energies, particularly solar energy, have become more complex and sophisticated over the past few years by making severe system outages. In light of increased automation in solar-based energy industries, a holistic cyber-physical infrastructure must be considered to predict the effect of CAs on electrical networks and the ways to enhance its resilience. The present study examines the resilience characteristics at the equipment area of the diverse control methods and their effect on the outage severity of the smart grid using digital twins (DT) simulation technology. The paper presents a machine learning based metric for measuring the resilience of cyber-physical features that considers device-level characteristics, vulnerabilities, and system models. Resiliency refers to a system's capability of providing energy even during severe contingencies and relates to the ability to resist, forecast, and recover. An example based on the newest CA against Ukraine has been provided and simulated on DT environment. A case study is proposed to illustrate how cyber-physical resilience metrics can be applied to improve operators' situational awareness and provide more proactive or corrective control measures for improving resilience. [ABSTRACT FROM AUTHOR]

The paper is devoted to the digital transformation of the core engineering maintenance processes involved in medium-voltage overhead distribution network infrastructure inspection. The study presents an analysis of digital twin utilization for inspection of the infrastructure of medium-voltage overhead distribution networks. At present, the infrastructure monitoring process is still manual, and its automatization is a challenging task due to the large distances between and small dimensions of the elements involved. The proposed digital twin is employed for 3D infrastructure modeling and complex analysis based on photogrammetry and aerial scanning data processing methods. The paper describes practical use cases for these data-driven methods for specific infrastructure management processes, e.g., scheduled inspection processes, including geometrical parameter measurements and visual infrastructure element defect identification, unplanned inspection processes (state of emergency, post-storm, etc.), and vegetation management processes. The proposed method allows operations to be performed remotely without physical presence in the field. At the same time, data-driven solutions provide objective results and potential automation via machine learning algorithms, which are more profitable economically. As the proposed method allows processes to be performed remotely, these data-driven solutions offer cost-effective results for automation. The digital twin concept is applied to the Latvian medium-voltage overhead distribution network with the support of the Latvian distribution system operator "Sadales tīkls" JSC. [ABSTRACT FROM AUTHOR]

While machine learning (ML) has made significant contributions to the biopharmaceutical field, its applications are still in the early stages in terms of providing direct support for quality-by-design based development and manufacturing of biologics, hindering the enormous potential for bioprocesses automation from their development to manufacturing. However, the adoption of ML-based models instead of conventional multivariate data analysis methods is significantly increasing due to the accumulation of large-scale production data. This trend is primarily driven by the real-time monitoring of process variables and quality attributes of biopharmaceutical products through the implementation of advanced process analytical technologies. Given the complexity and multidimensionality of a bioproduct design, bioprocess development, and product manufacturing data, ML-based approaches are increasingly being employed to achieve accurate, flexible, and high-performing predictive models to address the problems of analytics, monitoring, and control within the biopharma field. This paper aims to provide a comprehensive review of the current applications of ML solutions in the design, monitoring, control, and optimisation of upstream, downstream, and product formulation processes of monoclonal antibodies. Finally, this paper thoroughly discusses the main challenges related to the bioprocesses themselves, process data, and the use of machine learning models in monoclonal antibody process development and manufacturing. Moreover, it offers further insights into the adoption of innovative machine learning methods and novel trends in the development of new digital biopharma solutions. • Provide a review of applications of ML in antibody design and manufacturing. • Identifying challenges related to bioprocesses and process data. • Offering a valuable reference with a thorough discussion and public datasets. • Proposing machine learning (ML) research directions in antibody manufacturing. • Identifying advanced ML solutions for digital twins within biopharma 4.0. [ABSTRACT FROM AUTHOR]

• Digital twin solution is demonstrated for a CHS X-joint under fatigue loading. • Digital twin is capable of accurately identifying, locating and quantifying cracks. • Relationship between crack size and nearby strains is revealed via substructuring. • Crack diagnosis is performed with affordable strain sensors. • Physics-informed model is validated through strain-interfaced physical twins. This paper aims to introduce a strain-interfaced local digital twin solution for a welded circular hollow section (CHS) X-joint subjected to brace axial loading. The solution comprises a series of machine learning algorithms to (1) identify the presence of cracks, (2) locate the cracks and (3) quantify the extent of cracking. These algorithms make use of strain readings in the vicinity of the crack to perform the diagnosis, representing a remote sensing methodology, thereby eliminating physical inspections. The validation of the proposed methodology includes two experiments – one each in the high and low cycle fatigue regime – demonstrating its wide scale applicability. The success of these experiments highlights the strong potential of affordable strain sensors in crack diagnosis assessments for CHS joints. [ABSTRACT FROM AUTHOR]

Digital twins (DTs) have attracted widespread attention in academia and industry in recent years. It can accurately reflect the physical world in real-time, enabling online monitoring, control, and prediction operations. Their foundation is super-real-time computing and high data representation capabilities. However, current DTs do not achieve 3D super-real-time computing. This study proposes a novel 3D computational method for solving fluid–solid coupling problems in a super-real-time. The method is based on a mixed solution framework that combines traditional numerical methods with deep learning operators. Specifically, the method employs multi-core CPU parallel acceleration to solve the solid equations while leveraging the computing power of GPU to solve the fluid equations. The fluid–solid coupling is achieved through information exchange between the GPU and the multi-core CPU. In addition, the proposed method introduces a new deep learning operator framework based on the DeepONET. The framework is accompanied by a database structure that facilitates model training and validation and a loss function that guides the training. The space nuclear reactor, an improved TOPAZ-II system, was selected to demonstrate its feasibility. Four non-training transient conditions were simulated to test the generalization performance. The results show that the proposed method achieves an average error between the calculated results and reference values below 2.5%, with the average error of thermodynamic parameters below 1.5%. The average deviation between system parameter peak values during the transient process and the reference value was less than 5 s. The result meets the acceptable error level and satisfies the super-real-time requirements with a time acceleration ratio of approximately 1.17, which is 60 times faster than traditional numerical methods. The results demonstrate the accuracy and efficiency of the proposed method for DT. [ABSTRACT FROM AUTHOR]

[Display omitted] • Discusses the concept of building performance simulation versus new digital trends. • Reviews Artificial Intelligence, Machine Learning, Digital Twins, Cyber-Physical Systems, Internet of Things, Data Mining. • Explores common notions about differences, similarities, strengths and limitations of each concept. • Reviews emerging work on the interface between these concepts. • New concepts enjoy traction and energize the building performance research area. In an increasingly digital world, there are fast-paced developments in fields such as Artificial Intelligence, Machine Learning, Data Mining, Digital Twins, Cyber-Physical Systems and the Internet of Things. This paper reviews and discusses how these new emerging areas relate to the traditional domain of building performance simulation. It explores the boundaries between building simulation and these other fields in order to identify conceptual differences and similarities, strengths and limitations of each of these areas. The paper critiques common notions about these new domains and how they relate to building simulation, reviewing how the field of building performance may evolve and benefit from the new developments. [ABSTRACT FROM AUTHOR]

• Digital twin with AI to develop a predictive monitoring system that estimates CO2-eq and TVOC emissions from existing buildings. • The predictive monitoring system of CO2-eq emissions was developed using the design science research paradigm. • Real-life case study was used to validate the created solution. • This solution introduces interactive dashboard to predict Co2-eq based on varying humidity percentages and temperature degrees. The revolution of the industry 4.0 presents a new era of digital transformation for the construction industry, advancing towards the concept of digital twins, while on the other hand it faces the global challenge of reducing carbon emissions from operational assets. The current research gap in the application of digital twins for achieving net zero were reviewed in this study, highlighting its potentials for enhancement in the built environment, and emphasizing the need for demonstration of a use-case analysis for its adoption by the industry. This research presents a digital twin solution to automate the monitoring and controlling of equivalent carbon dioxide (eCO 2) emissions from existing assets through the integration of IoT, BIM, and artificial intelligence across a comprehensive solution, further validating its workability through a real-life use case analysis. The study revealed the significance of BIM and IoT, as essential components of a digital twin to visualise critical spatial information for enhanced facility management specifically for monitoring of indoor air quality of spaces, while also coalescing an AI-supported system to predict carbon emissions from the collected data through integration of machine learning features across the digital twin. The output of the entire solution is displayed as an interactive dashboard for observing trends and patterns, enabling stakeholders to implement effective data-driven retrofitting strategies. This research is a fundamental initiation for implementing digital twins to monitor emissions from existing assets, a step towards achieving the net zero targets. [ABSTRACT FROM AUTHOR]