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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]

A problem in nonlinear and complex dynamical systems with broad applications is forecasting the occurrence of a critical transition based solely on data without knowledge about the system equations. When such a transition leads to system collapse, as often is the case, all the available data are from the pre-critical regime where the system still functions normally, making the prediction problem challenging. In recent years, a machine-learning based approach tailored to solving this difficult prediction problem, adaptable reservoir computing, has been articulated. This Perspective introduces the basics of this machine-learning scheme and describes representative results. The general setting is that the system dynamics live on a normal attractor with oscillatory dynamics at the present time and, as a bifurcation parameter changes into the future, a critical transition can occur after which the system switches to a completely different attractor, signifying system collapse. To predict a critical transition, it is essential that the reservoir computer not only learns the dynamical "climate" of the system of interest at some specific parameter value but, more importantly, discovers how the system dynamics changes with the bifurcation parameter. It is demonstrated that this capability can be endowed into the machine through a training process with time series from a small number of distinct, pre-critical parameter values, thereby enabling accurate and reliable prediction of the catastrophic critical transition. Three applications are presented: predicting crisis, forecasting amplitude death, and creating digital twins of nonlinear dynamical systems. Limitations and future perspectives are discussed. [ABSTRACT FROM AUTHOR]

Herein, state‐of‐the‐art multiscale modeling methods have been described. This research includes notable molecular, micro‐, meso‐, and macroscale models for hard (polymer, metal, yarn, fiber, fiber‐reinforced polymer, and polymer matrix composites) and soft (biological tissues such as brain white matter [BWM]) composite materials. These numerical models vary from molecular dynamics simulations to finite‐element (FE) analyses and machine learning/deep learning surrogate models. Constitutive material models are summarized, such as viscoelastic hyperelastic, and emerging models like fractional viscoelastic. Key challenges such as meshing, data variability and material nonlinearity‐driven uncertainty, limitations in terms of computational resources availability, model fidelity, and repeatability are outlined with state‐of‐the‐art models. Latest advancements in FE modeling involving meshless methods, hybrid ML and FE models, and nonlinear constitutive material (linear and nonlinear) models aim to provide readers with a clear outlook on futuristic trends in composite multiscale modeling research and development. The data‐driven models presented here are of varying length and time scales, developed using advanced mathematical, numerical, and huge volumes of experimental results as data for digital models. An in‐depth discussion on data‐driven models would provide researchers with the necessary tools to build real‐time composite structure monitoring and lifecycle prediction models. [ABSTRACT FROM AUTHOR]

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

Emerging from the convergence of digital twin technology and the metaverse, consumer health (MCH) is witnessing a transformative shift. The amalgamation of bioinformatics with healthcare Big Data has ushered in a new era of disease prediction models that harness comprehensive medical data, enabling the anticipation of illnesses even before the onset of symptoms. In this model, deep neural networks stand out because they improve accuracy remarkably by increasing network depth and making weight changes using gradient descent. Nonetheless, traditional methods face their own set of challenges, including the issues of gradient instability and slow training. In this case, the Broad Learning System (BLS) stands out as a good alternative. It gets around the problems with gradient descent and lets you quickly rebuild a model through incremental learning. One problem with BLS is that it has trouble extracting complex features from complex medical data. This makes it less useful in a wide range of healthcare situations. In response to these challenges, we introduce DAE-BLS, a novel hybrid model that marries Denoising AutoEncoder (DAE) noise reduction with the efficiency of BLS. This hybrid approach excels in robust feature extraction, particularly within the intricate and multifaceted world of medical data. Validation using diverse datasets yields impressive results, with accuracies reaching as high as 98.50%. DAE-BLS's ability to rapidly adapt through incremental learning holds great promise for accurate and agile disease prediction, especially within the complex and dynamic healthcare scenarios of today. [ABSTRACT FROM AUTHOR]

With the ever increasing number of active satellites in space, the rising demand for larger formations of small satellites and the commercialization of the space industry (so-called New Space), the realization of manufacturing processes in orbit comes closer to reality. Reducing launch costs and risks, allowing for faster on-demand deployment of individually configured satellites as well as the prospect for possible on-orbit servicing for satellites makes the idea of realizing an in-orbit factory promising. In this paper, we present a novel approach to an in-orbit factory of small satellites covering a digital process twin, AI-based fault detection, and teleoperated robot-control, which are being researched as part of the "AI-enabled Cyber–Physical In-Orbit Factory" project. In addition to the integration of modern automation and Industry 4.0 production approaches, the question of how artificial intelligence (AI) and learning approaches can be used to make the production process more robust, fault-tolerant and autonomous is addressed. This lays the foundation for a later realization of satellite production in space in the form of an in-orbit factory. Central aspect is the development of a robotic AIT (Assembly, Integration and Testing) system where a small satellite could be assembled by a manipulator robot from modular subsystems. Approaches developed to improving this production process with AI include employing neural networks for optical and electrical fault detection of components. Force sensitive measuring and motion training helps to deal with uncertainties and tolerances during assembly. An AI-guided teleoperated control of the robot arm allows for human intervention while a Digital Process Twin represents process data and provides supervision during the whole production process. Approaches and results towards automated satellite production are presented in detail. • An automated production system for modular small satellites is proposed. • Central element is a force-sensitive robotic assembly, integration & testing system. • AI and machine learning approaches are applied to make the process more robust. • A digital process twin supervises the autonomous production. • A multi-modal shared control approach allows human teleoperation if required. [ABSTRACT FROM AUTHOR]

For a chemical looping hydrogen generation system, the set of input gas flow rate needed to be adjusted on the reactivity of oxygen carriers, in order to guarantee gas conversion and to obtain higher hydrogen purity. However, the oxygen carriers often decayed upon repeated cycles, leading to pressing need for dynamic alignment between oxygen carrier reactivity and process parameters. Herein, we propose a reaction optimization control method based on machine learning methods that can establish a real-time matching system between the flow rate of input gas and the reactivity of oxygen carriers. The results show that the prediction of hydrogen yield from oxygen carriers when using the optimized Gradient Boosting Decision Tree model is in general agreement with the experimental results (R2 = 0.87, MAE = 0.49). Based on high-precision prediction results, the method is capable of realizing the input gas flow based on the predicted hydrogen production in real time in less than 10s and realizing digital twin with the expansion of the dataset. We envision that the proposed control method can accelerate the industrial application of chemical looping hydrogen generation by predicting accurate operating parameters that can reduce fuel costs and guarantee reactivity of oxygen carriers. • Interpretable relationship between reaction variables and cyclic hydrogen production through machine learning modeling. • Hyperparameter optimization of machine learning models to solve overfitting problems. • The predicted hydrogen production for the new input oxygen carriers is in general agreement with the experimental results. • Digital twin system solves the problem of unstable reduction depth in chemical looping hydrogen production. [ABSTRACT FROM AUTHOR]

The catalytic hydrogenation of carbon dioxide (CO 2) to methanol presents a significant opportunity for both mitigating climate change and producing a valuable chemical feedstock. While existing reviews delve into diverse modeling strategies, the role and potential of machine learning (ML) approaches remain largely unexplored. This review addresses the gap by comprehensively exploring the mechanism, workflow, and application of ML models in the methanol production process. The review highlights the significance of ML application in catalytic CO 2 hydrogenation for methanol synthesis, emphasizing process optimization, predicting methanol performance indicators, thermodynamic modeling, reaction kinetics, and assessing catalyst activity. Furthermore, the review delves into cutting-edge approaches like hybrid models, gray-box models, and digital twins, showcasing their potential to revolutionize the methanol production process. This comprehensive review serves as a valuable resource for forthcoming research aimed at optimizing the CO 2 conversion process to efficiently and sustainably produce methanol. [Display omitted] • Machine learning models are extensively employed in catalytic hydrogenation to produce methanol. • This review is dedicated to the analysis and synthesis of these models as well as to their applications. • ML exhibits high accuracy in modeling CO 2 hydrogenation into methanol. • ML models predict key performance indicators of methanol and optimize reaction conditions. • Innovative ML, gray box, digital twin, and Hybrid algorithms leverage vast databases in this domain. [ABSTRACT FROM AUTHOR]

The recently proposed Chemical Reaction Neural Network (CRNN) discovers chemical reaction pathways from time resolved species concentration data in a deterministic manner. Since the weights and biases of a CRNN are physically interpretable, the CRNN acts as a digital twin of a classical chemical reaction network. In this study, we employ a Bayesian inference analysis coupled with neural ordinary differential equations (ODEs) on this digital twin to discover chemical reaction pathways in a probabilistic manner. This allows for estimation of the uncertainty surrounding the learned reaction network. To achieve this, we propose an algorithm which combines neural ODEs with a preconditioned stochastic gradient langevin descent (pSGLD) Bayesian framework, and ultimately performs posterior sampling on the neural network weights. We demonstrate the successful implementation of this algorithm on several reaction systems by not only recovering the chemical reaction pathways but also estimating the uncertainty in our predictions. We compare the results of the pSGLD with that of the standard SGLD and show that this optimizer more efficiently and accurately estimates the posterior of the reaction network parameters. Additionally, we demonstrate how the embedding of scientific knowledge improves extrapolation accuracy by comparing results to purely data-driven machine learning methods. Together, this provides a new framework for robust, autonomous Bayesian inference on unknown or complex chemical and biological reaction systems. [ABSTRACT FROM AUTHOR]

Lake thermal dynamics have been considerably impacted by climate change, with potential adverse effects on aquatic ecosystems. To better understand the potential impacts of future climate change on lake thermal dynamics and related processes, the use of mathematical models is essential. In this study, we provide a comprehensive review of lake water temperature modeling. We begin by discussing the physical concepts that regulate thermal dynamics in lakes, which serve as a primer for the description of process‐based models. We then provide an overview of different sources of observational water temperature data, including in situ monitoring and satellite Earth observations, used in the field of lake water temperature modeling. We classify and review the various lake water temperature models available, and then discuss model performance, including commonly used performance metrics and optimization methods. Finally, we analyze emerging modeling approaches, including forecasting, digital twins, combining process‐based modeling with deep learning, evaluating structural model differences through ensemble modeling, adapted water management, and coupling of climate and lake models. This review is aimed at a diverse group of professionals working in the fields of limnology and hydrology, including ecologists, biologists, physicists, engineers, and remote sensing researchers from the private and public sectors who are interested in understanding lake water temperature modeling and its potential applications. Plain Language Summary: Lake thermal dynamics are fundamental in controlling mixing processes and have significant implications for biological and geochemical processes. Consequently, the impacts of climate change on these dynamics can have severe consequences for the health of lakes and their aquatic ecosystems. In this context, mathematical models are essential for understanding the potential effects of future climate change on lake thermal dynamics and related processes. This manuscript offers a comprehensive review of lake water temperature modeling. It covers the fundamental physical concepts that govern thermal dynamics in lakes and provides an overview of various sources of observational water temperature data, including in situ monitoring and satellite data used in these models. The study evaluates different types of lake water temperature models, including statistical, process‐based, and hybrid models. It explores emerging modeling approaches such as forecasting, digital twins, combining process‐based modeling with deep learning, ensemble modeling, and climate‐lake models coupling. Model performance is also discussed, highlighting suggested evaluation metrics and providing a comprehensive analysis of the state‐of‐the‐art optimization methods to assess model accuracy. This review targets researchers in limnology, hydrology, ecology, biology, physics, engineering, and remote sensing from the private and public sectors interested in lake water temperature modeling and its applications. Key Points: Lake thermal dynamics are central in shaping mixing processes and the health of aquatic ecosystems, and climate change alters these dynamicsMathematical models are essential to understand past and project future climate change impacts on lake thermal dynamicsThis study reviews lake water temperature modeling, covering concepts, data sources, and model evaluation for applications across disciplines [ABSTRACT FROM AUTHOR]

Pressure swing adsorption(PSA) based gas separation technology is widely applied in chemical industries, especially in hydrogen purification. Its complex mechanism and non-steady state feature restrict the separation performance adjustment and application on efficient gas utilization. Process simulation, machine learning and modelling combined innovating gas management methodology is proposed in this paper to perform accurate gas separation for natural gas based syngas mixture. VB script program is employed to drive Aspen adsorption simulation, and then classical and regression tree(CART), polynomial regression(PNR) and surrogate model are comprehensively used to perform machine learning. Consequently, the coefficient matrix is obtained to quantify the separation performance. This method holds the potential for broader application in refineries and SPCs hybrid hydrogen network, facilitating the comprehensive utilization of hydrogen containing gases. Case study results show this methodology can conserve natural gas and gas productivity by 21.3 %, as well as carbon emission by 8211.6 Nm3/h. This work highlights the promising application of digital twin technology within the chemical industry, showcasing how process simulation, machine learning, and modeling can collectively revolutionize gas management and resource conservation. [Display omitted] • Novel PSA based gas allocation instead of gas separation technology. • Simulation, modelling and machine learning combined gas accommodating methodology. • An insight technology for integration of refinery and SPC. • A novel gas allocation method for zero waste of hydrogen. • An insight application of digital twin technology for gas efficient utilization. [ABSTRACT FROM AUTHOR]

This survey provides a comprehensive overview of traditional techniques and deep learning-based methodologies for monocular visual odometry (VO), with a focus on displacement measurement applications. This paper outlines the fundamental concepts and general procedures for VO implementation, including feature detection, tracking, motion estimation, triangulation, and trajectory estimation. This paper also explores the research challenges inherent in VO implementation, including scale estimation and ground plane considerations. The scientific literature is rife with diverse methodologies aiming to overcome these challenges, particularly focusing on the problem of accurate scale estimation. This issue has been typically addressed through the reliance on knowledge regarding the height of the camera from the ground plane and the evaluation of feature movements on that plane. Alternatively, some approaches have utilized additional tools, such as LiDAR or depth sensors. This survey of approaches concludes with a discussion of future research challenges and opportunities in the field of monocular visual odometry. [ABSTRACT FROM AUTHOR]

Technological improvements are crucial for achieving decarbonisation targets and addressing the impacts of climate change in the built environment via mitigation and adaptation measures. Data-driven methods for building performance prediction are particularly important in this regard. Nevertheless, the deployment of these technologies faces challenges, particularly in the domains of artificial intelligence (AI) ethics, interpretability and explainability of machine learning (ML) algorithms. The challenges encountered in applications for the built environment are amplified, particularly when data-driven solutions need to be applied throughout all the stages of the building life cycle and to address problems from a socio-technical perspective, where human behaviour needs to be considered. This requires a consistent use of analytics to assess the performance of a building, ideally by employing a digital twin (DT) approach, which involves the creation of a digital counterpart of the building for continuous analysis and improvement. This paper presents an in-depth review of the critical connections between data-driven methods, AI ethics, interpretability and their implementation in the built environment, acknowledging the complex and interconnected nature of these topics. The review is organised into three distinct analytical levels: The first level explores key issues of the current research on the interpretability of machine learning methods. The second level considers the adoption of interpretable data-driven methods for building energy modelling and the problem of establishing a link with the third level, which examines physics-driven grey-box modelling techniques, in order to provide integrated modelling solutions. The review's findings highlight how the interpretability concept is relevant in multiple contexts pertaining to energy and the built environment and how some of the current knowledge gaps can be addressed by further research in the broad area of data-driven methods. [ABSTRACT FROM AUTHOR]

This paper focuses on the feasibility of deep neural operator network (DeepONet) as a robust surrogate modeling method within the context of digital twin (DT) enabling technology for nuclear energy systems. Machine learning (ML)-based prediction algorithms that need extensive retraining for new reactor operational conditions may prohibit real-time inference for DT across varying scenarios. In this study, DeepONet is trained with possible operational conditions and that relaxes the requirement of continuous retraining - making it suitable for online and real-time prediction components for DT. Through benchmarking and evaluation, DeepONet exhibits remarkable prediction accuracy and speed, outperforming traditional ML methods, making it a suitable algorithm for real-time DT inference in solving a challenging particle transport problem. DeepONet also exhibits generalizability and computational efficiency as an efficient surrogate tool for DT component. However, the application of DeepONet reveals challenges related to optimal sensor placement and model evaluation, critical aspects of real-world DT implementation. Addressing these challenges will further enhance the method's practicality and reliability. Overall, this study marks an important step towards harnessing the power of DeepONet surrogate modeling for real-time inference capability within the context of DT enabling technology for nuclear systems. [ABSTRACT FROM AUTHOR]

The use of renewable energy sources is becoming increasingly widespread around the world due to various factors, the most relevant of which is the high environmental friendliness of these types of energy resources. However, the large-scale involvement of green energy leads to the creation of distributed energy networks that combine several different generation methods, each of which has its own specific features, and as a result, the data collection and processing necessary to optimize the operation of such energy systems become more relevant. Development of new technologies for the more optimal use of RES is one of the main tasks of modern research in the field of energy, where an important place is assigned to the use of technologies based on artificial intelligence, allowing researchers to significantly increase the efficiency of the use of all types of RES within energy systems. This paper proposes to consider the methodology of application of modern approaches to the assessment of the amount of energy obtained from renewable energy sources based on artificial intelligence technologies, approaches used for data processing and for optimization of the control processes for operating energy systems with the integration of renewable energy sources. The relevance of the work lies in the formation of a general approach applied to the evaluation of renewable energy sources such as solar and wind energy based on the use of artificial intelligence technologies. As a verification of the approach considered by the authors, a number of models for predicting the amount of solar power generation using photovoltaic panels have been implemented, for which modern machine-learning methods have been used. As a result of testing for quality and accuracy, the best results were obtained using a hybrid forecasting model, which combines the joint use of a random forest model applied at the stage of the normalization of the input data, exponential smoothing model, and LSTM model. [ABSTRACT FROM AUTHOR]

Background: Trees are a critical part of urban infrastructure. Cities worldwide are pledging afforestation objectives due to net-zero targets; however, their realisation requires a comprehensive framework that combines science, policy, and practice. Methods: The paper presents the Green Urban Scenarios (GUS) framework for designing and monitoring green infrastructures. GUS considers weather, maintenance, tree species, diseases, and spatial distributions of trees to forecast their impacts. The framework uses agent-based modelling (ABM) and simulation paradigm to integrate green infrastructure into a city’s ecological, spatial, economic, and social context. ABM enables the creation of digital twins for urban ecosystems at any level of granularity, including individual trees, to accurately predict their future trajectories. Digital representation of trees is created using a combination of datasets such as earth observations from space, street view images, field surveys, and qualitative descriptions of typologies within existing and future projects. Machine learning and statistical models calibrate biomass growth patterns and carbon release schemes. Results: The paper examines various green area typologies, simulating several hypothetical scenarios based on Glasgow’s urban forests. It exhibits the emergence of heterogeneity features of the forests due to interactions among trees. The growth trajectory of trees has a non-linear transition phase toward stable growth in its maturity. Reduced maintenance deteriorates the health of trees leading to lower survival rate and increased CO2 emissions, while the stormwater alleviation capacity may differ among species. Conclusions: The paper demonstrates how GUS can facilitate policies and maintenance of urban forests with environmental, social, and economic benefits. [ABSTRACT FROM AUTHOR]

The information that automated train control (ATO) systems use to improve safety and reduce power usage is limited by on-board and wayside monitoring applications and computing power. This paper presents an augmented digital twin for railway applications that enables real-time consideration of derailment risk in train operations. The augmented digital twin implements a surrogate model with the results of a massive multibody dynamics numerical program and machine learning models to predict the instantaneous wagon derailment risk. A case study for a heavy haul iron ore wagon with three-piece bogies was conducted to test the augmented digital twin. A multibody simulation numerical program comprising 2100 simulation cases was completed. The surrogate model was developed using linear, polynomial, decision tree and ensemble forest regression models on the results of the numerical program. A longitudinal train simulator was used to calculate the speed and lateral coupler force throughout a train trip. The surrogate model effectively predicted the derailment index for empty and loaded conditions accounting for lateral coupler forces, vehicle speeds and curve radius. The proposed augmented digital twin can be further developed to accomplish other train operational benefits such as the reduction of rail damage. [ABSTRACT FROM AUTHOR]

The paper shows the feasibility of building closed-form and fast-to-evaluate surrogate models via supervised kernel-based machine learning regressions behaving as digital twins for computationally expensive multibody simulations. The aforementioned surrogate models are adopted to predict the railway vehicle dynamics safety indexes defined in the international standards, depending on the wheel-rail forces, directly from the results of longitudinal train dynamics simulations. The digital twin models are trained with the outputs of Simpack multibody simulations of a reference freight wagon, to which the in-train forces calculated by an in-house MATLAB longitudinal train dynamics simulator are applied. Two machine learning techniques are considered: the support vector machine and the least-squares support vector machine regressions. Both techniques ensure a good accuracy even with a limited number of training samples. The derivation of the surrogate models can strongly enhance the modelling capabilities of common longitudinal train dynamics simulators, that cannot evaluate the wheel-rail contact forces. At the same time, the method shown in the paper allows to strongly reduce the total computational times, as the evaluation of the closed-form surrogate models allows to effectively estimate the safety indexes with no need for computationally demanding multibody simulations. [ABSTRACT FROM AUTHOR]

This review paper provides a summary of methods in which artificial intelligence (AI) techniques have been applied in the management of variable renewable energy (VRE) systems, and an outlook to future directions of research in the field. The VRE types included are namely solar, wind and marine varieties. AI techniques, and particularly machine learning (ML), have gained traction as a result of data explosion, and offer a method for integration of multimodal data for more accurate forecasting in energy applications. The VRE management aspects in which AI techniques have been applied include optimized power generation forecasting and integration of VRE into power grids, including the aspects of demand forecasting, energy storage, system optimization, performance monitoring, and cost management. Future directions of research in the applications of AI for VRE management are proposed and discussed, including the issue of data availability, types and quality, in addition to explainable artificial intelligence (XAI), quantum artificial intelligence (QAI), coupling AI with the emerging digital twins technology, and natural language processing. [ABSTRACT FROM AUTHOR]

Modern solutions for precise fault localisation in Low Voltage (LV) Distribution Networks (DNs) often rely on costly tools such as the micro-Phasor Measurement Unit (μ PMU), which is potentially impractical for the large number of nodes in LVDNs. This paper introduces a novel fault detection technique using a distribution network digital twin without the use of μ PMUs. The Digital Twin (DT) integrates data from Smart Meters (SMs) and network topology to create an accurate replica. In using SM voltage-magnitude readings, the pre-built twin compiles a database of fault scenarios and matches them with their unique voltage fingerprints. However, this SM-based voltage-only approach shows only a 70.7% accuracy in classifying fault type and location. Therefore, this research suggests using the cables' Currents Symmetrical Component (CSC). Since SMs do not provide direct current data, a Machine Learning (ML)-based regression method is proposed to estimate the cables' currents in the DT. Validation is performed on a 41-node LV distribution feeder in the Scottish network provided by the industry partner Scottish Power Energy Networks (SPEN). The results show that the current estimation regressor significantly improves fault localisation and identification accuracy to 95.77%. This validates the crucial role of a DT in distribution networks, thus enabling highly accurate fault detection when using SM voltage-only data, with further refinements being conducted through estimations of CSC. The proposed DT offers automated fault detection, thus enhancing customer connectivity and maintenance team dispatch efficiency without the need for additional expensive μ PMU on a densely-noded distribution network. [ABSTRACT FROM AUTHOR]

Summary: Wireless multimodal interactions over long distance (WMILD) would give rise to numerous thrilling applications, such as remote touching and immersive teleoperations. However, long distances can induce large propagation delays, which makes it difficult to meet the ultra‐low latency requirements in haptic‐visual interactions. Considering existing works mainly focused on the wireless access part, this paper designs an end‐to‐end framework for general WMILD applications based on the digital twin (DT) technology and proposes an intelligent resource allocation and parameter compression scheme to guarantee WMILD performance under constraint network resources. In the framework, user device can acquire real‐time remote interactions by performing local interactions with nearby base station (BS), where a DT of the remote side is deployed to predict the remote haptic‐visual feedbacks. A reliable DT updating process is carefully designed to guarantee the DT accurately model its dynamic physical counterpart. To optimize the updating reliability, we formulate the resource allocation and parameter compression to be a constraint‐Markov decision problem, under the constraints on energy consumption, multimodal interactions and updating latencies. Then, a safe deep reinforcement learning algorithm is proposed to adapt resources and compression according to the dynamic DT updating workload, multimodal data‐streams and remote transmission capacities. Simulation shows the framework can achieve high updating reliability compared with baselines. [ABSTRACT FROM AUTHOR]

The crossbeam is frequently subjected to alternating loads during work as an essential load-bearing part of the crane. However, due to the large volume and the limitations of detection technology, it is impossible to realize online monitoring of the mechanical state. The ongoing advancement of ROMing and digital twin technology plays a pivotal role in facilitating the resolution of this particular issue. In this paper, we take the crane beam as the physical entity and combine the Twin Builder reduced-order technology and Deployer digital twin deployment technology to establish a digital twin of the beam. The load recognition model within the twin system exhibits a prediction error rate of ±5%. Furthermore, the accuracy of the ROM surpasses that of conventional machine learning models by a factor of 25. Upon deployment on the web platform, the results are delivered within 0.5 s, representing a substantial improvement as it is merely 1/15 of the time required for traditional 3D displays. The digital twin online monitoring system has the advantages of high accuracy and low requirements for monitoring equipment, which can be widely used in engineering practice to solve the problem that the mechanical state of large parts cannot be accurately monitored online. [ABSTRACT FROM AUTHOR]

The pharmaceutical industry has entered an era of transformation with the emergence of Pharma 4.0, which leverages cutting-edge technologies in manufacturing processes. These hold tremendous potential for enhancing the overall efficiency, safety, and quality of non-biological complex drugs (NBCDs), a category of pharmaceutical products that pose unique challenges due to their intricate composition and complex manufacturing requirements. This review attempts to provide insight into the application of select Pharma 4.0 technologies, namely machine learning, in silico modeling, and 3D printing, in the manufacturing process of NBCDs. Specifically, it reviews the impact of these tools on NBCDs such as liposomes, polymeric micelles, glatiramer acetate, iron carbohydrate complexes, and nanocrystals. It also addresses regulatory challenges associated with the implementation of these technologies and presents potential future perspectives, highlighting the incorporation of digital twins in this field of research as it seems to be a very promising approach, namely for the optimization of NBCDs manufacturing processes. [ABSTRACT FROM AUTHOR]

Environmental effects may lead to cracking, stiffness loss, brace damage, and other damages in bridges, frame structures, buildings, etc. Structural Health Monitoring (SHM) technology could prevent catastrophic events by detecting damage early. In recent years, Deep Learning (DL) has developed rapidly and has been applied to SHM to detect, localize, and evaluate diverse damages through efficient feature extraction. This paper analyzes 337 articles through a systematic literature review to investigate the application of DL for SHM in the operation and maintenance phase of facilities from three perspectives: data, DL algorithms, and applications. Firstly, the data types in SHM and the corresponding collection methods are summarized and analyzed. The most common data types are vibration signals and images, accounting for 80% of the literature studied. Secondly, the popular DL algorithm types and application areas are reviewed, of which CNN accounts for 60%. Then, this article carefully analyzes the specific functions of DL application for SHM based on the facility's characteristics. The most scrutinized study focused on cracks, accounting for 30 percent of research papers. Finally, challenges and trends in applying DL for SHM are discussed. Among the trends, the Structural Health Monitoring Digital Twin (SHMDT) model framework is suggested in response to the trend of strong coupling between SHM technology and Digital Twin (DT), which can advance the digitalization, visualization, and intelligent management of SHM. [ABSTRACT FROM AUTHOR]

Artificial intelligence covers a variety of methods and disciplines including vision, perception, speech and dialogue, decision making and planning, problem solving, robotics and other applications in which self-learning is possible. The aim of this work was to study the possibilities of using AI algorithms at various stages of construction to ensure the safety of the process. The objects of this research were scientific publications about the use of artificial intelligence in construction and ways to optimize this process. To search for information, Scopus and Web of Science databases were used for the period from the early 1990s (the appearance of the first publication on the topic) until the end of 2022. Generalization was the main method. It has been established that artificial intelligence is a set of technologies and methods used to complement traditional human qualities, such as intelligence as well as analytical and other abilities. The use of 3D modeling for the design of buildings, machine learning for the conceptualization of design in 3D, computer vision, planning for the effective use of construction equipment, artificial intelligence and artificial superintelligence have been studied. It is proven that automatic programming for natural language processing, knowledge-based systems, robots, building maintenance, adaptive strategies, adaptive programming, genetic algorithms and the use of unmanned aircraft systems allow an evaluation of the use of artificial intelligence in construction. The prospects of using AI in construction are shown. [ABSTRACT FROM AUTHOR]

Autonomous network management is crucial for Fifth Generation (5G) and Beyond 5G (B5G) networks, where a constantly changing environment is expected and network configuration must adapt accordingly. Modeling tools are required to predict the impact on performance (packet and delay loss) when new traffic demands arrives and when changes in routing paths are applied in the network. Mathematical analysis and network simulators are techniques for modeling networks but both have limitations, as the former provides low accuracy and the latter requires high execution times. To overcome these limitations, machine learning (ML) algorithms, and more specifically, graph neural networks (GNNs), are proposed for network modeling due to their ability to capture complex relationships from graph-like data while predicting network properties with high accuracy and low computational requirements. However, one of the main issues when using GNNs is their lack of generalization capability to larger networks, i.e., when trained in small networks (in number of nodes, paths length, links capacity), the accuracy of predictions on larger networks is poor. This paper addresses the GNN generalization problem by the use of fundamental networking concepts. Our solution is built from a baseline GNN model called RouteNet (developed by Barcelona Neural Networking Center-Universitat Politècnica de Catalunya (BNN-UPC)) that predicts the average delay in network paths, and through a number of simple additions significantly improves the prediction accuracy in larger networks. The improvement ratio compared to the baseline model is 101, from a 187.28% to a 1.828%, measured by the Mean Average Percentage Error (MAPE). In addition, we propose a closed-loop control context where the resulting GNN model could be potentially used in different use cases. [ABSTRACT FROM AUTHOR]

Recent developments in networked and smart sensors have significantly changed the way Structural Health Monitoring (SHM) and asset management are being carried out. Since the sensor networks continuously provide real-time data from the structure being monitored, they constitute a more realistic image of the actual status of the structure where the maintenance or repair work can be scheduled based on real requirements. This review is aimed at providing a wealth of knowledge from the working principles of sensors commonly used in SHM, to artificial-intelligence-based digital twin systems used in SHM and proposes a new asset management framework. The way this paper is structured suits researchers and practicing experts both in the fields of sensors as well as in asset management equally. [ABSTRACT FROM AUTHOR]

This review article provides a critical assessment of the progress made in computational modelling of metal-based additive manufacturing (AM) with emphasis on its ability to predict physical phenomena, concepts of microstructural evolution, residual stresses, role of multiple thermal cycles, and formation of multi-dimensional defects along with the achieved degree of experimental validation. The uniqueness of this article stems from the inclusion of comprehensive information on computational progress in the field of fusion-based, sintering-based, and mechanical deformation-based AM. A computational model's role in determining the process framework for the desired outcome of the set properties of the AM components is recognised while presenting the process-microstructure maps, thereby appraising computational ability towards the qualification of products. The inclusion of a detailed discussion on the bi-directional coupling of machine learning and physics-based computational models provides a futuristic roadmap for the digital twin of metal-based AM. [ABSTRACT FROM AUTHOR]

We propose a novel technique to accurately predict carbon dioxide (CO2) concentrations even in flow fields with temperature gradients based on a single laser path absorption spectrum measurement and machine learning. Concentration measurements in typical tunable diode laser absorption spectroscopy are based on a ratio of two integrated absorbances, each from a spectral line with different temperature dependence. However, the inferred concentrations can deviate significantly from the actual concentrations in the presence of temperature gradients. Furthermore, it is also difficult to find an analytical expression to compensate for the effect of nonuniform temperature profiles on concentration measurements. In this study, the entire absorption feature was considered since its shape and peak intensities vary with temperature and concentration. Specifically, a predictive model is obtained in a data-driven manner that can identify and compensate for the effect of a nonuniform temperature field on the spectrum. Despite a very detailed understanding of the CO2 absorption spectrum, it is nearly impossible to collect sufficient spectra for model acquisition by varying all temperature gradient conditions. Therefore, the model was obtained using only simulated data, much like the concept of a "digital twin". Finally, the predictive performance of the acquired model was verified using experimental data. In all test cases, the predictive performance of the model was superior to that of the two-line method. Additionally, a gradient-weighted regression activation mapping analysis confirmed that the model utilizes both the peak intensities as well as the change in the shape of absorption lines for prediction. Graphical Abstract This is a visual representation of the abstract. [ABSTRACT FROM AUTHOR]

Largely unknown just a few decades ago, computational systems biology is now a central methodology for biological and medical research. This amazing ascent raises the question of what the community should do next. The article outlines our personal vision for the future of computational systems biology, suggesting the need to address both mindsets and methodologies. We present this vision by focusing on current and anticipated research goals, the development of strong computational tools, likely prominent applications, education of the nextgeneration of scientists, and outreach to the public. In our opinion, two classes of broad research goals have emerged in recent years and will guide future efforts. The first goal targets computational models of increasing size and complexity, aimed at solving emerging health-related challenges, such as realistic whole-cell and organ models, disease simulators and digital twins, in silico clinical trials, and clinically translational applications in the context of therapeutic drug development. Such large models will also lead us toward solutions to pressing issues in agriculture and environmental sustainability, including sufficient food availability and life in changing habitats. The second goal is a deep understanding of the essence of system designs and strategies with which nature solves problems. This understanding will help us explain observed biological structures and guide forays into synthetic biological systems. Regarding effective methodologies, we suggest efforts toward automated data pipelines from raw biomedical data all the way to spatiotemporal mechanistic model. These will be supported by dynamic methods of statistics, machine learning, artificial intelligence and streamlined strategies of dynamic model design, striking a fine balance between modeling realistic complexity and abstracted simplicity. Finally, we suggest the need for a concerted, community-wide emphasis on effective education in systems biology, implemented as a combination of formal instruction and hands-on mentoring. The educational efforts should furthermore be extended toward the public through books, blogs, social media, and interactive networking opportunities, with the ultimate goal of training in state-of-the-art technology while recapturing the lost art of synthesis. [ABSTRACT FROM AUTHOR]

This review paper addresses the critical need for structural prognostics and health management (SPHM) in aircraft maintenance, highlighting its role in identifying potential structural issues and proactively managing aircraft health. With a comprehensive assessment of various SPHM techniques, the paper contributes by comparing traditional and modern approaches, evaluating their limitations, and showcasing advancements in data-driven and model-based methodologies. It explores the implementation of machine learning and deep learning algorithms, emphasizing their effectiveness in improving prognostic capabilities. Furthermore, it explores model-based approaches, including finite element analysis and damage mechanics, illuminating their potential in the diagnosis and prediction of structural health issues. The impact of digital twin technology in SPHM is also examined, presenting real-life case studies that demonstrate its practical implications and benefits. Overall, this review paper will inform and guide researchers, engineers, and maintenance professionals in developing effective strategies to ensure aircraft safety and structural integrity. [ABSTRACT FROM AUTHOR]

The operation of the electricity network has grown more complex due to the increased adoption of renewable energy resources, such as wind and solar power. Using energy storage technology can improve the stability and quality of the power grid. One such technology is flywheel energy storage systems (FESSs). Compared with other energy storage systems, FESSs offer numerous advantages, including a long lifespan, exceptional efficiency, high power density, and minimal environmental impact. This article comprehensively reviews the key components of FESSs, including flywheel rotors, motor types, bearing support technologies, and power electronic converter technologies. It also presents the diverse applications of FESSs in different scenarios. The progress of state-of-the-art research is discussed, emphasizing the use of artificial intelligence methods such as machine learning, digital twins, and data-driven techniques for system simulation, fault prediction, and life-assessment research. The article also addresses the challenges related to current research and the application of FESSs. It concludes by summarizing future directions and trends in FESS research, offering valuable information for further advancement and improvement in this field. [ABSTRACT FROM AUTHOR]

Digital twins (DTs) are becoming increasingly popular in various industries, and their potential for healthcare in the metaverse continues to attract attention. The metaverse is a virtual world where individuals interact with digital replicas of themselves and the environment. This paper focuses on personalized and precise medicine and examines the current application of DTs in healthcare within the metaverse. Healthcare practitioners may use immersive virtual worlds to replicate medical scenarios, improve teaching experiences, and provide personalized care to patients. However, the integration of DTs in the metaverse poses technical, regulatory, and ethical challenges that need to be addressed, including data privacy, standards, and accessibility. Through this examination, we aim to provide insights into the transformative potential of DTs in healthcare within the metaverse and encourage further research and development in this exciting domain. [ABSTRACT FROM AUTHOR]

One of the challenges in the field of human activity recognition in smart homes based on IoT sensors is the variability in the recorded data. This variability arises from differences in home configurations, sensor network setups, and the number and habits of inhabitants, resulting in a lack of data that accurately represent the application environment. Although simulators have been proposed in the literature to generate data, they fail to bridge the gap between training and field data or produce diverse datasets. In this article, we propose a solution to address this issue by leveraging the concept of digital twins to reduce the disparity between training and real-world data and generate more varied datasets. We introduce the Virtual Smart Home, a simulator specifically designed for modeling daily life activities in smart homes, which is adapted from the Virtual Home simulator. To assess its realism, we compare a set of activity data recorded in a real-life smart apartment with its replication in the VirtualSmartHome simulator. Additionally, we demonstrate that an activity recognition algorithm trained on the data generated by the VirtualSmartHome simulator can be successfully validated using real-life field data. [ABSTRACT FROM AUTHOR]

The concept of a digital twin is intriguing as it presents an innovative approach to solving numerous real-world challenges. Initially emerging from the domains of manufacturing and engineering, digital twin research has transcended its origins and now finds applications across a wide range of disciplines. This multidisciplinary expansion has impressively demonstrated the potential of digital twin research. While the simulation aspect of a digital twin is often emphasized, the role of artificial intelligence (AI) and machine learning (ML) is severely understudied. For this reason, in this paper, we highlight the pivotal role of AI and ML for digital twin research. By recognizing that a digital twin is a component of a broader Digital Twin System (DTS), we can fully grasp the diverse applications of AI and ML. In this paper, we explore six AI techniques—(1) optimization (model creation), (2) optimization (model updating), (3) generative modeling, (4) data analytics, (5) predictive analytics and (6) decision making—and their potential to advance applications in health, climate science, and sustainability. [ABSTRACT FROM AUTHOR]

The global objective of achieving net-zero emissions drives a significant electrified trend by replacing fuel-mechanical systems with onboard microgrid (OBMG) systems for transportation applications. Energy management strategies (EMS) for OBMG systems require complicated optimization algorithms and high computation capabilities, while traditional control techniques may not meet these requirements. Driven by the ability to achieve intelligent decision-making by exploring data, artificial intelligence (AI) and digital twins (DT) have gained much interest within the transportation sector. Currently, research on EMS for OBMGs primarily focuses on AI technology, while overlooking the DT. This article provides a comprehensive overview of both information technology, particularly elucidating the role of DT technology. The evaluation and analysis of those emerging information technologies are explicitly summarized. Moreover, this article explores potential challenges in the implementation of AI and DT technologies and subsequently offers insights into future trends. [ABSTRACT FROM AUTHOR]

In this work, a soft sensor–based digital twin (DT) was developed to reduce the startup time in manufacturing plastic tubes and enable real-time product quality monitoring, i.e., the weight per unit length and the inner and outer diameters of the tube. An experimental campaign was conducted on a real tube extrusion line using three polyvinyl chloride (PVC) compounds and different process conditions, and machine learning regression algorithms were trained and tested to create the models of the extruder and the extrusion die the DT is based on. The characterization of the considered material, whose properties were given as input to the digital models, was carried out according to a procedure based only on the data collected by the production line. The DT was tested for the startup of the production of a single-layer tube and allowed to achieve the specified customer requirements (thickness and weight) in a few minutes. The proposed solution thus proved to be a valuable tool for reducing the setup time, thus increasing the efficiency of the process. [ABSTRACT FROM AUTHOR]

High efficiency and safety are critical factors in ensuring the optimal performance and reliability of systems and equipment across various industries. Fault monitoring (FM) techniques play a pivotal role in this regard by continuously monitoring system performance and identifying the presence of faults or abnormalities. However, traditional FM methods face limitations in fully capturing the complex interactions within a system and providing real-time monitoring capabilities. To overcome these challenges, Digital Twin (DT) technology has emerged as a promising solution to enhance existing FM practices. By creating a virtual replica or digital copy of a physical equipment or system, DT offers the potential to revolutionize fault monitoring approaches. This paper aims to explore and discuss the diverse range of predictive methods utilized in DT and their implementations in FM across industries. Furthermore, it will showcase successful implementations of DT in FM across a wide array of industries, including manufacturing, energy, transportation, and healthcare. The utilization of DT in FM enables a comprehensive understanding of system behavior and performance by leveraging real-time data, advanced analytics, and machine learning algorithms. By integrating physical and virtual components, DT facilitates the monitoring and prediction of faults, providing valuable insights into the system's health and enabling proactive maintenance and decision making. [ABSTRACT FROM AUTHOR]

Digital twins derived from 3D scanning data were developed to measure soft tissue deformation in head and neck surgery by an artificial intelligence approach. This framework was applied suggesting feasibility of soft tissue shift detection as a hitherto unsolved problem. In a pig head cadaver model 104 soft tissue resection had been performed. The surface of the removed soft tissue (RTP) and the corresponding resection cavity (RC) was scanned (N = 416) to train an artificial intelligence (AI) with two different 3D object detectors (HoloLens 2; ArtecEva). An artificial tissue shift (TS) was created by changing the tissue temperature from 7,91±4,1°C to 36,37±1,28°C. Digital twins of RTP and RC in cold and warm conditions had been generated and volumes were calculated based on 3D surface meshes. Significant differences in number of vertices created by the different 3D scanners (HoloLens2 51313 vs. ArtecEva 21694, p<0.0001) hence result in differences in volume measurement of the RTC (p = 0.0015). A significant TS could be induced by changing the temperature of the tissue of RC (p = 0.0027) and RTP (p = <0.0001). RC showed more correlation in TS by heating than RTP with a volume increase of 3.1 μl or 9.09% (p = 0.449). Cadaver models are suitable for training a machine learning model for deformable registration through creation of a digital twin. Despite different point cloud densities, HoloLens and ArtecEva provide only slightly different estimates of volume. This means that both devices can be used for the task.TS can be simulated and measured by temperature change, in which RC and RTP react differently. This corresponds to the clinical behaviour of tumour and resection cavity during surgeries, which could be used for frozen section management and a range of other clinical applications. [ABSTRACT FROM AUTHOR]

Digital twin is the digital representation of an entity, and it drives Industry 4.0. This paper presents a bibliometric analysis of digital twin in the supply chain to help researchers, industry practitioners, and academics to understand the trend, development, and focus of the areas of digital twin in the supply chain. This paper found several key clusters of research, including the designing of a digital twin model, integration of a digital twin model, application of digital twin in quality control, and digital twin in digitalization. In the embryonic stage of research, digital twin was tested in the production line with limited optimization. In the development stage, the importance of digital twin in Industry 4.0 was observed, as big data, machine learning, Industrial Internet of Things, blockchain, edge computing, and cloud-based systems complemented digital twin models. Digital twin was applied to improve sustainability in manufacturing and production logistics. In the current prosperity stage with high annual publications, the recent trends of this topic focus on the integration of deep learning, data models, and artificial intelligence for digitalization. This bibliometric analysis also found that the COVID-19 pandemic drove the start of the prosperity stage of digital twin research in the supply chain. Researchers in this field are slowly moving towards applying digital twin for human-centric systems and mass personalization to prepare to transit to Industry 5.0. [ABSTRACT FROM AUTHOR]

Scanning underwater areas using magnetometers in search of unexploded ordnance is a difficult challenge, where machine learning methods can find a significant application. However, this requires the creation of a dataset enabling the training of prediction models. Such a task is difficult and costly due to the limited availability of relevant data. To address this challenge in the article, we propose the use of numerical modeling to solve this task. The conducted experiments allow us to conclude that it is possible to obtain high compliance with the numerical model based on the finite element method with the results of physical tests. Additionally, the paper discusses the methodology of simplifying the computational model, allowing for an almost three times reduction in the calculation time without affecting model quality. The article also presents and discusses the methodology for generating a dataset for the discrimination of UXO/non-UXO objects. According to that methodology, a dataset is generated and described in detail including assumptions on objects considered as UXO and nonUXO. [ABSTRACT FROM AUTHOR]

The increasing proportion of bifacial photovoltaic modules (Bi-PVM) in new projects makes the operation of photovoltaic system (PVS) more complicated, and it is difficult to accurately predict the power of the PVS. To solve this problem, this paper proposes a new power prediction method for PVS based on Bi-PVM. Firstly, the equal proportion digital twin model of the example project is constructed. The superposition principle is used to analyze the factors affecting the power generation performance of Bi-PVM, and the characteristic project is constructed according to the analysis results. Secondly, the bifacial correction coefficient is introduced to reduce the parameter error caused by Bi-PVM to the prediction model. On this basis, a power prediction machine learning model based on bidirectional gated recurrent unit (Bi-GRU) network is established. Finally, a simulation experiment is carried out on the TensorFlow machine learning platform. With the actual operation data of a PV power station in Jiuquan, China, the simulation analysis is carried out under four weather types, namely, sunny day, rainy day, snowy day and complex and changeable day, respectively, which verified the correctness and excellence of the proposed method. [ABSTRACT FROM AUTHOR]

Intelligent digital twins closely resemble their real-life counterparts. In health and medical care, they enable the real-time monitoring of patients, whereby large amounts of data can be collected to produce actionable information. These powerful tools are constructed with the aid of artificial intelligence, machine learning, and deep learning; the Internet of Things; and cloud computing to collect a diverse range of digital data (e.g., from digital patient journals, wearable sensors, and digitized monitoring equipment or processes), which can provide information on the health conditions and therapeutic responses of their physical twins. Intelligent digital twins can enable data-driven clinical decision making and advance the realization of personalized care. Migraines are a highly prevalent and complex neurological disorder affecting people of all ages, genders, and geographical locations. It is ranked among the top disabling diseases, with substantial negative personal and societal impacts, but the current treatment strategies are suboptimal. Personalized care for migraines has been suggested to optimize their treatment. The implementation of intelligent digital twins for migraine care can theoretically be beneficial in supporting patient-centric care management. It is also expected that the implementation of intelligent digital twins will reduce costs in the long run and enhance treatment effectiveness. This study briefly reviews the concept of digital twins and the available literature on digital twins for health disorders such as neurological diseases. Based on these, the potential construction and utility of digital twins for migraines will then be presented. The potential and challenges when implementing intelligent digital twins for the future management of migraines are also discussed. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) has received much attention in the domain of railway traffic planning and management (TPM) from academia and industries. While many promising applications have been reported, there remains a lack of detailed review of the many AI models/algorithms and their uses and adaptations in rail TPM. To fill this gap, this systematic literature review conducts, reports, and synthesizes the state-of-the-art of AI applied in railway TPM from four perspectives, i.e., the intersection between AI research fields (e.g., expert systems, data mining, and adversarial search) and rail TPM, the intersection between AI techniques (e.g., evolutionary computing and machine learning) and rail TPM, the intersection between AI applications (e.g., operations research, scheduling, and planning) and rail TPM, and the intersection between AI related disciplines (e.g., big data analytics and digital twins) and rail TPM. The study evaluates 95 research papers published during 1970–2022. Accordingly, a comprehensive synthesis of each intersection between AI and rail TPM is presented, and the practical roadmap for application of AI in rail TPM is proposed. Furthermore, the study identifies the research gaps and areas that need more investigation. The contribution helps researchers and practitioners to get a better understanding of the status quo of research stream, research development trends, and challenges for further related study. [ABSTRACT FROM AUTHOR]

The drug discovery process has been a challenging task for any pharmaceutical industry. In ancient times the discovery of new drug molecules was made on an experimental basis until computers were introduced in the field of Pharmaceutical sciences, which led to a breakthrough in pharmaceutical research and development. Computer-Aided Drug Design (CADD) revolutionized the drug discovery process by minimizing the time and showing accurate results in the drug discovery process. Artificial Intelligence (AI) and Machine Learning (ML) are the advanced version of CADD technologies for the drug discovery process. The integrated technologies of both AI and machine learning have unleashed the immense potential of researchers in the pharmaceutical industries and drug discovery process, leading to significant innovations and the development of intelligent technologies. This review highlights some of them as it focuses on the concept of industrialization 4.0 (smart industries), which involves CADD's most progressive and innovative ideas like automation, augmented reality, digital twins, cobots, and computational fluid dynamics. These are the advanced form of CADD technologies, which have immense potential for designing smart industries. All these have led the pharmaceutical industries to evolve from man to machine, which results in complete automation. [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]

Structural health monitoring (SHM) of bridges is constantly upgraded by researchers and bridge engineers as it directly deals with bridge performance and its safety over a certain time period. This article addresses some issues in the traditional SHM systems and the reason for moving towards an automated monitoring system. In order to automate the bridge assessment and monitoring process, a mechanism for the linkage of Digital Twins (DT) and Machine Learning (ML), namely the Support Vector Machine (SVM) algorithm, is discussed in detail. The basis of this mechanism lies in the collection of data from the real bridge using sensors and is providing the basis for the establishment and calibration of the digital twin. Then, data analysis and decision-making processes are to be carried out through regression-based ML algorithms. So, in this study, both ML brain and a DT model are merged to support the decision-making of the bridge management system and predict or even prevent further damage or collapse of the bridge. In this way, the SHM system cannot only be automated but calibrated from time to time to ensure the safety of the bridge against the associated damages. [ABSTRACT FROM AUTHOR]

Exact observing and forecasting tool conditions fundamentally affect cutting execution, bringing further developed workpiece machining accuracy and lower machining costs. Because of the unpredictability and time-differing nature of the cutting system, existing methodologies cannot achieve ideal oversight progressively. A technique dependent on Digital Twins (DT) is proposed to accomplish extraordinary accuracy in checking and anticipating tool conditions. This technique builds up a balanced virtual instrument framework that matches entirely with the physical system. Collecting data from the physical system (Milling Machine) is initialized, and sensory data collection is carried out. The National Instruments data acquisition system captures vibration data through a uni-axial accelerometer, and a USB-based microphone sensor acquires the sound signals. The data are trained with different Machine Learning (ML) classification-based algorithms. The prediction accuracy is calculated with the help of a confusion matrix with the highest accuracy of 91% through a Probabilistic Neural Network (PNN). This result has been mapped by extracting the statistical features of the vibrational data. Testing has been performed with the trained model to validate the model's accuracy. Later, the modeling of the DT is initiated using MATLAB-Simulink. This model has been created under the data-driven approach. The physical–virtual balance of the DT model is acknowledged utilizing the advances, taking into consideration the detailed planning of the constant state of the tool's condition. The tool condition monitoring system through the DT model is deployed through the machine learning technique. The DT model can predict the different tool conditions based on sensory data. [ABSTRACT FROM AUTHOR]

Structural damage is inherent in civil engineering structures, and bridges are no exception. It is vital to monitor and keep track of damage in bridge structures as a result of multiple mechanical, environmental and traffic-induced factors. Monitoring the formation and propagation of structural damage is also pertinent for enhancing the service life of bridges. Bridge health monitoring (BHM) has always been an active research area for engineers and stakeholders. While all monitoring techniques intend to provide accurate and decisive information on the remaining useful life, safety, integrity and serviceability of bridges, maintaining uninterrupted operation of a bridge relies strongly on understanding the development and propagation of damage. BHM methods have been extensively researched on bridges over the decades, and new methodologies have started to be used by domain experts, especially within the last decade. Emerging methods, as the products of technology advancements, have resulted in handy tools that have been quickly adopted by bridge engineers. State-of-the-art techniques such as lidar, photogrammetry, virtual reality, augmented reality, digital twins, computer vision, machine learning and deep learning are now integrated parts of the new-generation of BHM operations. This paper presents a brief overview of these latest BHM technologies. [ABSTRACT FROM AUTHOR]

While the Metaverse is becoming a popular trend and drawing much attention from academia, society, and businesses, processing cores used in its infrastructures need to be improved, particularly in terms of signal processing and pattern recognition. Accordingly, the speech emotion recognition (SER) method plays a crucial role in creating the Metaverse platforms more usable​ and enjoyable for its users. However, existing SER methods continue to be plagued by two significant problems in the online environment. The shortage of adequate engagement and customization between avatars and users is recognized as the first issue and the second problem is related to the complexity of SER problems in the Metaverse as we face people and their digital twins or avatars. This is why developing efficient machine learning (ML) techniques specified for hypercomplex signal processing is essential to enhance the impressiveness and tangibility of the Metaverse platforms. As a solution, echo state networks (ESNs), which are an ML powerful tool for SER, can be an appropriate technique to enhance the Metaverse's foundations in this area. Nevertheless, ESNs have some technical issues restricting them from a precise and reliable analysis, especially in the aspect of high-dimensional data. The most significant limitation of these networks is the high memory consumption caused by their reservoir structure in face of high-dimensional signals. To solve all problems associated with ESNs and their application in the Metaverse, we have come up with a novel structure for ESNs empowered by octonion algebra called NO2GESNet. Octonion numbers have eight dimensions, compactly display high-dimensional data, and improve the network precision and performance in comparison to conventional ESNs. The proposed network also solves the weaknesses of the ESNs in the presentation of the higher-order statistics to the output layer by equipping it with a multidimensional bilinear filter. Three comprehensive scenarios to use the proposed network in the Metaverse have been designed and analyzed, not only do they show the accuracy and performance of the proposed approach, but also the ways how SER can be employed in the Metaverse platforms. • An octonion based nonlinear echo state network framework is proposed. • The proposed approach is developed to analyzing hypercomplex problems. • Three practical scenarios have been proposed to SER in the Metaverse. • Developed approach is successfully tested on EMODB. [ABSTRACT FROM AUTHOR]