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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]

With the wide application of power cables in the field of transmission and distribution and the increasing emphasis of power departments on the reliability, safety and stability of power cable operation, how to more accurately and quickly analyze the temperature distribution of power cables and how to evaluate the running state of power cables have become research hotspots. Through the combination of finite element calculation and the artificial intelligence method, an innovative method of digital twin cable temperature prediction based on RF-GPR is proposed in this paper. Firstly, the finite element method is used to calculate the coupling of the electromagnetic field and temperature field of a 10 kV AC cable laid in the cable trench, and a certain amount of basic data are provided through the finite element calculation results. Then, using the basic principle of the random forest (RF) variable importance score, the RF-GPR cable temperature prediction model is constructed using the series hybrid model and Gaussian process regression (GPR), the model prediction results are compared and analyzed, and the calculation time is improved by about 1500 times. Finally, a digital twinning platform for cable temperature calculation based on RF-GPR is designed, which provides technical support for the application of digital twinning. [ABSTRACT FROM AUTHOR]

Battery management systems (BMSs) are critical to ensure the efficiency and safety of high-power battery energy storage systems (BESSs) in vehicular and stationary applications. Recently, the proliferation of battery big data and cloud computing advancements has led to the development of a new generation of BMSs, named Cloud BMS (CBMS), aiming to improve the performance and safety of BESSs. The CBMS is a cyber-physical system with connectivity between the physical BMS and a cloud-based virtual BMS, which is realized through a communication channel such as Internet of Things. Compared to the traditional BMS, the CBMS offers significantly higher computational resources, leveraging the implementation of advanced digital twin models and best-in-class algorithms in the BMS software, which will provide superior performances. However, as for any other CPS, the CBMS creates vulnerabilities against cyberattacks and if not properly secured, could end up damaging the BESS and/or causing dangerous, expensive, and life-threatening situations. Cybersecurity of the CBMSs has thus become a trending topic and several works have been published in this area in recent years. This paper conducts a scoping review to address different topics related to BMS cybersecurity. The CBMS architecture is presented, and the potential cyberattack surfaces are identified. Different possible attack scenarios, including attack points, attack types, and their impact at the component level (BMS and BESS) and system level (vehicle or grid), are discussed. In addition, the paper provides a review of potential countermeasures to protect the CBMS against cyberattacks. The paper also includes a review of the applicable standards and regulations that relate to this trending topic. Finally, based on the reviewed gaps, potential future research domains on BMS cybersecurity topics are identified and presented at the end of the paper. [ABSTRACT FROM AUTHOR]

This document discusses the emergence of deep learning as a transformative tool in the field of operations research (OR). Deep learning, a specialized category of machine learning algorithms, uses multiple layers to uncover hidden patterns and insights from large datasets. It has gained popularity due to its superior predictive performance compared to traditional machine learning models. The integration of deep learning applications into OR has the potential to enhance decision-making, productivity, sustainability, and optimization processes. However, there are challenges to address, such as data availability, interpretability, and computational cost. The document highlights the need for practical research and provides future research directions in areas such as supply chain management, demand forecasting, inventory optimization, and decision-making processes. Overall, deep learning has the potential to transform the field of OR, and this document aims to provide new perspectives for further research and practical applications. [Extracted from the article]

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.

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 next-generation 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.

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]

Muscle electrophysiology has emerged as a powerful tool to drive human machine interfaces, with many new recent applications outside the traditional clinical domains, such as robotics and virtual reality. However, more sophisticated, functional, and robust decoding algorithms are required to meet the fine control requirements of these applications. Deep learning has shown high potential in meeting these demands, but requires a large amount of high-quality annotated data, which is expensive and time-consuming to acquire. Data augmentation using simulations, a strategy applied in other deep learning applications, has never been attempted in electromyography due to the absence of computationally efficient models. We introduce a concept of Myoelectric Digital Twin - highly realistic and fast computational model tailored for the training of deep learning algorithms. It enables simulation of arbitrary large and perfectly annotated datasets of realistic electromyography signals, allowing new approaches to muscular signal decoding, accelerating the development of human-machine interfaces. Muscle electrophysiology is a promising tool for human-machine approaches in medicine and beyond clinical applications. The authors propose here a model simulating electric signals produced during human movements and apply this data for training of deep learning algorithms. [ABSTRACT FROM AUTHOR]

The technology of visual servoing, with the digital twin as its driving force, holds great promise and advantages for enhancing the flexibility and efficiency of smart manufacturing assembly and dispensing applications. The effective deployment of visual servoing is contingent upon the robust and accurate estimation of the vision-motion correlation. Network-based methodologies are frequently employed in visual servoing to approximate the mapping between 2D image feature errors and 3D velocities, offering promising avenues for improving the accuracy and reliability of visual servoing systems. These developments have the potential to fully leverage the capabilities of digital twin technology in the realm of smart manufacturing. However, obtaining sufficient training data for these methods is challenging, and thus improving model generalization to reduce data requirements is imperative. To address this issue, we offer a learning-based approach for estimating Jacobian matrices of visual servoing that organically combines an extreme learning machine (ELM) and a differential evolutionary algorithm (DE). In the first stage, the pseudoinverse of the image Jacobian matrix is approximated using the ELM, which solves the problems associated with traditional visual servoing and is resistant to outside influences such as image noise and mistakes in camera calibration. In the second stage, differential evolution is utilized to select input weights and hidden layer bias and to determine ELM's output weights. Experimental results conducted on a digital twin operating platform for 4-DOF robot with an eye-in-hand configuration demonstrate better performance than classical visual servoing and traditional ELM-based visual servoing in various cases. [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]

Transient car emission tests generate huge amount of test data, but their results are usually evaluated only using their "accumulated" cycle values according to the homologation limits. In this work, two machine learning models were developed and applied to a truck RDE test and two light-duty vehicle chassis emission tests. Different from the conventional approach, the engine parameters and fuel consumption were acquired from the Engine Control Unit, not from the test measurement equipment. Instantaneous engine values were used as input in machine-learning-based digital twins. This novel approach allows for much less costly vehicle tests and optimizations. The paper's novel approach and developed digital twins model were able to predict both instantaneous and accumulated fuel consumption with good accuracy, and also for tests cycles different to the one used to train the model. [ABSTRACT FROM AUTHOR]

The development of digital twins leads to the pathway toward intelligent buildings. Today, the overwhelming rate of data in buildings carries a high amount of information that can provide an opportunity for a digital representation of the buildings and energy optimization strategies in the Heating, Ventilation, and Air Conditioning (HVAC) systems. To implement a successful energy management strategy in a building, a data-driven approach should accurately forecast the HVAC features, in particular the indoor temperatures. Accurate predictions not only increase thermal comfort levels, but also play a crucial role in saving energy consumption. This study aims to investigate the capabilities of data-driven approaches and the development of a model for predicting indoor temperatures. A case study of an educational building is considered to forecast indoor temperatures using machine learning and deep learning algorithms. The algorithms' performance is evaluated and compared. The important model parameters are sorted out before choosing the best architecture. Considering real data, prediction models are created for indoor temperatures. The results reveal that all the investigated models are successful in predicting indoor temperatures. Hence, the proposed deep neural network model obtained the highest accuracy with an average RMSE of 0.16 °C, which renders it the best candidate for the development of a digital twin. [ABSTRACT FROM AUTHOR]

In the era of industry 5.0, digital twins (DTs) play an increasingly pivotal role in contemporary society. Despite the literature's lack of a consistent definition, DTs have been applied to numerous areas as virtual replicas of physical objects, machines, or systems, particularly in manufacturing, production, and operations. One of the major advantages of digital twins is their ability to supervise the system's evolution and run simulations, making them connected and capable of supporting decision-making. Additionally, they are highly compatible with artificial intelligence (AI) as they can be mapped to all data types and intelligence associated with the physical system. Given their potential benefits, it is surprising that the utilization of DTs for warehouse management has been relatively neglected over the years, despite its importance in ensuring supply chain and production uptime. Effective warehouse management is crucial for ensuring supply chain and production continuity in both manufacturing and retail operations. It also involves uncertain material handling operations, making it challenging to control the activity. This paper aims to evaluate the synergies between AI and digital twins as state-of-the-art technologies and examines warehouse digital twins' (WDT) use cases to assess the maturity of AI applications within WDT, including techniques, objectives, and challenges. We also identify inconsistencies and research gaps, which pave the way for future development and innovation. Ultimately, this research work's findings can contribute to improving warehouse management, supply chain optimization, and operational efficiency in various industries. [ABSTRACT FROM AUTHOR]

Diamond cutting-tool wear has a direct impact on the processing accuracy of the machined surface in ultra-precision diamond cutting. It is difficult to monitor the tool's condition because of the slight wear amount. This paper proposed a hybrid deep learning model for tool wear state prediction in ultra-precision diamond cutting. The cutting force was accurately estimated and the wear state of the diamond tool was predicted by using the hybrid deep learning model with the motion displacement, velocity, and other signals in the machining process. By carrying out machining experiments, this method can classify diamond-tool wear condition with an accuracy of more than 85%. Meanwhile, the effectiveness of the proposed method was verified by comparing it with a variety of machine learning models. [ABSTRACT FROM AUTHOR]

Pharmaceutical manufacturing processes are moving towards automation and real-time process monitoring with the help of process analytical technologies (PATs) and predictive process models representing the real system. In this paper, we present a digital twin developed for an adjuvant manufacturing process involving a microfluidic formation of lipid particles. The twin uses a hybrid model for estimating the current state of the process and predicting system behavior in real time. The twin is used to control the adjuvant particle size, a critical quality attribute, by varying process parameters such as the temperature and inlet flow rates. We describe steps in the design and implementation of the twin, starting from the conception of the mechanistic model, up to the generation of its surrogate model used as state estimator, PATs and the setup of the information technology—Operational technology architecture. We demonstrate the performance of the twin by introducing different disturbances in the process and comparing the effect on the product critical quality attributes with and without the control of the digital twin. Finally, we showcase the digital twin implementation for the process in good manufacturing practice, through an engineering run, which demonstrated the robustness of the process when controlled by the digital twin. [ABSTRACT FROM AUTHOR]

The development and optimization of lipid nanoparticle (LNP) formulations through hydrodynamic mixing is critical for ensuring the efficient and cost-effective supply of vaccines. Continuous LNP formation through microfluidic mixing can overcome manufacturing bottlenecks and enable the production of nucleic acid vaccines and therapeutics. Predictive process models developed within a QbD Biopharma 4.0 approach can ensure the quality and consistency of the manufacturing process. This study highlights the importance of continuous LNP formation through microfluidic mixing in ensuring high-quality, in-specification production. Both empty and nucleic acid-loaded LNPs are characterized, followed by a TFF/buffer exchange to obtain process parameters for the envisioned continuous SPTFF. It is shown that LNP generation by pipetting leads to a less preferable product when compared to continuous mixing due to the heterogeneity and large particle size of the resulting LNPs (86–104 nm). Particle size by continuous formation (71 nm) and the achieved encapsulation efficiency (EE) of 88% is close to the targeted parameters for Pfizer's mRNA vaccine (66–93 nm, 88%EE). With the continuous encapsulation of nucleic acids in LNPs and the continuous production of mRNA in in vitro transcription, the basis for the holistic continuous production of mRNA is now established. We already showed that a fully autonomous process requires the incorporation of digital twins and a control strategy, with predictive process models and state-of-the-art PAT enabling real-time-release testing. This autonomous control can considerably improve productivity by about 15–20% and personnel as well as chemical reduction of about 30%. The results of this work complement this, laying the basis for fully continuous, bottleneck-free production of mRNA and other cell- and gene-therapeutic drug/vaccine candidates in a GMP- and QbD-compliant Biopharma 4.0 facilities on a flexible scale. [ABSTRACT FROM AUTHOR]

A digital twin basin serves as a virtual representation of a physical basin, enabling synchronous simulation, virtual–real interaction, and iterative optimization. The construction of a digital twin basin requires a basin database characterized by large-scale coverage, high-precision, high-resolution, and low-latency attributes. The advancements in remote sensing technology present a new technical means for acquiring essential variables of the basin. The purpose of this paper was to provide a comprehensive overview and discussion of the retrieval principle, data status, evaluation and inter-comparison, advantages and challenges, applications, and prospects of remote sensing technology in capturing seven essential variables, i.e., precipitation, surface temperature, evapotranspiration, water level, river discharge, soil moisture, and vegetation. It is indicated that remote sensing can be applied in some digital twin basin functions, such as drought monitoring, precipitation forecasting, and water resources management. However, more effort should be paid to improve the data accuracy, spatiotemporal resolution, and latency through data merging, data assimilation, bias correction, machine learning algorithms, and multi-sensor joint retrieval. This paper will assist in advancing the application of remote sensing technology in constructing a digital twin basin. [ABSTRACT FROM AUTHOR]

This research paper delves into the exploration of computer vision technology and digital twins as a means to enhance security measures in educational institutions. The study primarily focuses on the creation of a virtual replica of the first floor of a school and the integration of a person detection algorithm with the existing surveillance cameras. By leveraging the capabilities of the digital twin and real-time monitoring, comprehensive surveillance of the premises becomes feasible, resulting in simplified security operations. The paper sheds light on the significant potential of training neural networks for specific security tasks, such as the identification of weapons or the detection of anomalies in human behavior. These trained neural networks can be seamlessly integrated into the digital twin, thus ensuring public safety within the educational environment. The findings of this study provide substantial evidence for the effectiveness of computer vision technology and digital twins in bolstering security measures. The ability to create a virtual representation of the school's first floor enables comprehensive monitoring and surveillance, aiding in the prevention and prompt response to security incidents. The integration of person detection algorithms further enhances the system's capabilities by automatically identifying and tracking individuals within the premises. Additionally, the deployment of neural networks for specialized security tasks adds an extra layer of protection, enabling the identification of potential threats and the detection of abnormal behavior patterns. By employing computer vision technology and digital twins, educational institutions can establish an advanced security infrastructure that optimizes monitoring, enhances situational awareness, and ensures a safer environment for students, staff, and visitors. The research presented in this paper highlights the tremendous potential and practical implications of these technologies in the realm of educational security. [ABSTRACT FROM AUTHOR]

With an increasing number of offshore wind farms, monitoring and evaluating the effects of the wind turbines on the marine environment have become important tasks. Here we conducted a feasibility study with the focus on monitoring these effects by utilizing different machine learning methods. A multi-source dataset for a study site in the North Sea is created by combining satellite data, local in situ data and a hydrodynamic model. The machine learning algorithm DTWkNN, which is based on dynamic time warping and k-nearest neighbor, is used for multivariate time series data imputation. Subsequently, unsupervised anomaly detection is performed to identify possible inferences in the dynamic and interdepending marine environment around the offshore wind farm. The anomaly results are analyzed in terms of location, density and temporal variability, granting access to information and building a basis for explanation. Temporal detection of anomalies with COPOD is found to be a suitable method. Actionable insights are the direction and magnitude of potential effects of the wind farm on the marine environment, depending on the wind direction. This study works towards a digital twin of offshore wind farms and provides a set of methods based on machine learning to monitor and evaluate offshore wind farm effects, supporting stakeholders with information for decision making on future maritime energy infrastructures. [ABSTRACT FROM AUTHOR]

Urbanisation is leading to a concentration of growing city populations that contribute significantly to economic growth, while becoming epicentres of waste generation, greenhouse gas emissions, and food consumption. Nutrient smart city circular economy is currently an understudied intersection of growing city populations of food consumers, nutrient recovery technologies, Internet of Things (IoT), and agriculture. Meanwhile, machine learning has exploded with popularity over the years, with many circular economy literatures examining its usefulness in its predictive qualities to support management, optimisation, and recovery of useful resources from organic waste. This review paper examines advancements in machine learning for macronutrient recovery in city organic waste systems for a circular economy. The use of ML will greatly improve the scalability, transparency, productivity and accuracy of nutrient: recovery technologies, logistics, dissemination, and reuse. ML can also be combined with hardware to automate tedious waste separation, recovery and agricultural tasks using drones, hydroponics and satellites. Meanwhile, crop yields, nutrient demand-supply efficiencies, food security, environmental soil monitoring, and prosumer involvement could all increase. However, ML applications for urine, anaerobic digestion and prosumer economics are lacking. [Display omitted] • Lack of ML urine, membrane-AD, and prosumer studies for nutrient recovery. • IoT hardware, drones, democratised data sharing to address ML underfitting. • Lack of digital twins; CE BIM; good technologies and economics were barriers. • ML could increase transparency and food security for building consumer trust. • ML IoT to predict nutrients for soil, hydroponics, water treatment and farming. [ABSTRACT FROM AUTHOR]

This review paper was written on the occasion of the 40th anniversary of the Drying Technology Journal. Interestingly, most of the progress made in the domain of drying modeling and simulation has occurred over the last 40 years. Indeed, 40 years ago, the comprehensive formulation of coupled heat and mass transfer in porous media was available; however, its potential use for computational simulation was only in its nascent stage. This is therefore an ideal time span in which to take a full historical context, to present the state-of-the-art and to give insights into its future trajectory. All aspects are deliberately presented in a pedagogical manner with many illustrations to make the text accessible to young scientists. However, for the benefit of experts, detailed formulations are provided in the annexes. The main points addressed in this work include: The description of the comprehensive macroscopic formulation, An historical perspective of the progress made in computational simulation, The contribution of modeling in parameter characterization, The mechanical aspects of drying: strain and stress induced by drying, including the case of large deformations, The impact of non-local equilibrium, The promising domain of the digital twin in parameter tuning and control/command. [ABSTRACT FROM AUTHOR]

Modern manufacturing has to cope with dynamic and changing circumstances. Market fluctuations, the effects caused by unpredictable material shortages, highly variable product demand, and worker availability all require system robustness, flexibility, and resilience. To adapt to these new requirements, manufacturers should consider investigating, investing in, and implementing system autonomy. Autonomy is being adopted in multiple industrial contexts, but divergences arise when formalizing the concept of autonomous systems. To develop an implementation of autonomous manufacturing systems, it is essential to specify what autonomy means, how autonomous manufacturing systems are different from other autonomous systems, and how autonomous manufacturing systems are identified and achieved through the main features and enabling technologies. With a comprehensive literature review, this paper provides a definition of autonomy in the manufacturing context, infers the features of autonomy from different engineering domains, and presents a five-level model of autonomy — associated with maturity levels for the features — to ensure the complete identification and evaluation of autonomous manufacturing systems. The paper also presents the evaluation of a real autonomous system that serves as a use-case and a validation of the model. [ABSTRACT FROM AUTHOR]

Digital Twin (DT) is a virtual representation of a product system that exhibits the properties and analyzes the system's functions. The significant impact of DT extends to several fields, which increases productivity and reduces wastage. This article focuses on developing a Digital twin model of a Lathe machine for Tool Condition Monitoring (TCM). DT implementation in industries is challenging due to simulating online cutting forces and wear. Even though several pieces of research have been carried out in the prediction of tool conditions using machine learning, Artificial Neural network models, only a few pieces of research have been made in digital twins for TCM. This article provides the technique for implementing the DT model of a lathe tool. The feasibility of the DT Model framework is verified by a case study of the turning process with a CNC Lathe machine while machining of Aluminium 5052 workpiece using Titanium Nitride coated tool inserts. The sensor's data are acquired and fed to the microcontroller for real-time data acquisition. The real-time dataset is processed in the DT model for monitoring and predicting the tool conditions. The tool wear classification using the DT model is achieved. Developing the Digital Twin model in machining increases productivity and assists in predictive maintenance. [ABSTRACT FROM AUTHOR]

Robotic systems have become a standard tool in modern manufacturing due to their unique characteristics, such as repeatability, precision, and speed, among others. One of the main challenges of robotic manipulators is the low degree of reliability. Low reliability increases the probability of disruption in manufacturing processes, minimizing in this way the productivity and by extension the profit of the company. To address the abovementioned challenges, this research work proposes a robotic cell reliability optimization method based on digital twin and predictive maintenance. Concretely, the simulation of the robot is provided, and emphasis is given to the reliability optimization of the robotic cell's critical component. A supervised machine learning model is trained, aiming to detect and classify the faulty behavior of the critical component. Furthermore, a framework is proposed for the remaining useful life prediction with the aim to improve the reliability of the robotic cell. Thus, following the results of the current research work, appropriate maintenance tasks can be applied, preventing the robotic cell from serious failures and ensuring high reliability. [ABSTRACT FROM AUTHOR]

Featured Application: Maintaining production systems within Industry 4.0 facilitates the application of artificial intelligence methods, techniques and tools to predict potential failures and to take maintenance actions in advance at the right time and in the right way to avoid or minimize their harmful impact, including using digital twins. Maintenance of production equipment has a key role in ensuring business continuity and productivity. Determining the implementation time and the appropriate selection of the scope of maintenance activities are necessary not only for the operation of industrial equipment but also for effective planning of the demand for own maintenance resources (spare parts, people, finances). A number of studies have been conducted in the last decade and many attempts have been made to use artificial intelligence (AI) techniques to model and manage maintenance. The aim of the article is to discuss the possibility of using AI methods and techniques to anticipate possible failures and respond to them in advance by carrying out maintenance activities in an appropriate and timely manner. The indirect aim of these studies is to achieve more effective management of maintenance activities. The main method applied is computational analysis and simulation based on the real industrial data set. The main results show that the effective use of preventive maintenance requires large amounts of reliable annotated sensor data and well-trained machine-learning algorithms. Scientific and technical development of the above-mentioned group of solutions should be implemented in such a way that they can be used by companies of equal size and with different production profiles. Even relatively simple solutions as presented in the article can be helpful here, offering high efficiency at low implementation costs. [ABSTRACT FROM AUTHOR]

Among the diverse challenges in machining processes, chatter has a significant detrimental effect on surface quality and tool life, and it is a major limitation factor in achieving higher material removal rate. Early detection of chatter occurrence is considered a key element in the milling process automation. Online detection of chatter onset has been continually investigated over several decades, along with the development of new signal processing and machining condition classification approaches. This paper presents a review of the literature on chatter detection in milling, providing a comprehensive analysis of the reported methods for sensing and testing parameter design, signal processing and various features proposed as chatter indicators. It discusses data-driven approaches, including the use of different techniques in the time–frequency domain, feature extraction, and machining condition classification. The review outlines the potential of using multiple sensors and information fusion with machine learning. To conclude, research trends, challenges and future perspectives are presented, with the recommendation to study the tool wear effects, and chatter detection at dissimilar milling conditions, while utilization of considerable large datasets—Big Data—under the Industry 4.0 framework and the development of machining Digital Twin capable of real-time chatter detection are considered as key enabling technologies for intelligent manufacturing. [ABSTRACT FROM AUTHOR]

Medical digital twins, which represent medical assets, play a crucial role in connecting the physical world to the metaverse, enabling patients to access virtual medical services and experience immersive interactions with the real world. One serious disease that can be diagnosed and treated using this technology is cancer. However, the digitalization of such diseases for use in the metaverse is a highly complex process. To address this, this study aims to use machine learning (ML) techniques to create real-time and reliable digital twins of cancer for diagnostic and therapeutic purposes. The study focuses on four classical ML techniques that are simple and fast for medical specialists without extensive Artificial Intelligence (AI) knowledge, and meet the requirements of the Internet of Medical Things (IoMT) in terms of latency and cost. The case study focuses on breast cancer (BC), the second most prevalent form of cancer worldwide. The study also presents a comprehensive conceptual framework to illustrate the process of creating digital twins of cancer, and demonstrates the feasibility and reliability of these digital twins in monitoring, diagnosing, and predicting medical parameters. [ABSTRACT FROM AUTHOR]

Due to its automatic feature learning ability and high performance, deep learning has gradually become the mainstream of artificial intelligence in recent years, playing a role in many fields. Especially in the medical field, the accuracy rate of deep learning even exceeds that of doctors. This paper introduces several deep learning algorithms: Artificial Neural Network (NN), FM-Deep Learning, Convolutional NN and Recurrent NN, and expounds their theory, development history and applications in disease prediction; we analyze the defects in the current disease prediction field and give some current solutions; our paper expounds the two major trends in the future disease prediction and medical field—integrating Digital Twins and promoting precision medicine. This study can better inspire relevant researchers, so that they can use this article to understand related disease prediction algorithms and then make better related research. [ABSTRACT FROM AUTHOR]

This paper deals with state of the art in structural health monitoring (SHM) methods in offshore and marine structures. Most SHM methods have been developed for onshore infrastructures. Few studies are available to implement SHM technologies in offshore and marine structures. This paper aims to fill this gap and highlight the challenges in implementing SHM methods in offshore and marine structures. The present work categorises the available techniques for establishing SHM models in oil rigs, offshore wind turbine structures, subsea systems, vessels, pipelines and so on. Additionally, the capabilities of proposed ideas in recent publications are classified into three main categories: model-based methods, vibration-based methods and digital twin methods. Recently developed novel signal processing and machine learning algorithms are reviewed and their abilities are discussed. Developed methods in vision-based and population-based approaches are also presented and discussed. The aim of this paper is to provide guidelines for selecting and establishing SHM in offshore and marine structures. [ABSTRACT FROM AUTHOR]

Output-only structural health monitoring is a highly active research direction because it is a promising methodology for building digital twin applications providing near-real-time monitoring results of the structure. However, one of the technical bottlenecks is how to work effectively with multiple high-dimensional vibration signals. To address this question, this study develops a two-stage data-driven framework based on various advanced techniques, such as time-series feature extractions, self-learning, graph neural network, and machine learning algorithms. At first, multiple features in statistical, time, and spectral domains, are extracted from raw vibration data; then, they subsequently enter a graph convolution network to account for the spatial correlation of sensor locations. After that, the high-performance adaptive boosting machine learning algorithm is leveraged to assess structures' health states. This method allows for learning a lower-dimensional yet informative representation of vibration data; thus, the subsequent monitoring tasks could be performed with reduced time complexity and economical computational resources. The performance of the proposed method is qualitatively and quantitatively demonstrated through two examples involving both numerical and experimental structural data. Furthermore, comparison and robustness studies are carried out, showing that the proposed approach outperforms various machine learning/deep learning-based methods in terms of accuracy and noise/missing-robustness. [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]

In the area of industrial Internet of Things (IIoT), digital twins (DTs) are a powerful means for process improvement. In this paper the concept of a DT is explained and analysis possibilities throughout the life-cycle of a product and its production system are explored. The main part of this paper is focused on an approach to the analysis of manufacturing layouts and their parameters. The approach, which is based on a state of the art bottleneck detection method, allows an intelligent representation of the temporal process characteristics. The presented method is widely applicable for any type of manufacturing layout and time-span. The use of elementary heuristics leads to traceable results that can be used for further analysis or optimization. The results of this analysis method can be integrated in a DT and combined with machine learning and explainable artificial intelligence (XAI). The concept for a self-learning DT is explained and implementation possibilities are elucidated. [ABSTRACT FROM AUTHOR]

The collection of reliable data is an important and time-consuming part of the life cycle inventory (LCI) phase. Automation of individual steps can help to obtain a higher volume of or more realistic data. The aim of this paper is to survey the current state of automation potential in the scientific literature published between 2008 and 2021, with a focus on LCI in the area of process engineering. The results show that automation was most frequently found in the context of process simulation (via interfaces between software), for LCI database usage (e.g., via using ontologies for linking data) and molecular structure models (via machine learning processes such as artificial neural networks), which were also the categories where the highest level of maturity of the models was reached. No further usage could be observed in the areas of automation techniques for exploiting plant data, scientific literature, process calculation, stoichiometry and proxy data. The open science practice of sharing programming codes, software or other newly created resources was only followed in 20% of cases, uncertainty evaluation was only included in 10 out of 30 papers and only 30% of the developed methods were used in further publication, always including at least one of the first authors. For these reasons, we recommend encouraging exchange in the LCA community and in interdisciplinary settings to foster long-term sustainable development of new automation methodologies supporting data generation. [ABSTRACT FROM AUTHOR]

Multi-access edge computing (MEC) has unique interests in processing intensive and delay-critical computation tasks for vehicular application through computation offloading. However, due to the spatial inhomogeneity and dynamic mobility of connected vehicles (CVs), the edge servers (ESs) must dynamically adjust their resource allocation schemes to effectively provide computation offloading services for CVs. In this case, we propose a MEC framework supporting the collaboration of CVs, and incorporate digital twins (DTs) into wireless network to mitigate the unreliable long-distance communication between CVs and ESs. To solve the contradiction between the task change requirements of CVs and ES resources, we proactively balance the computation resources load of ESs by appropriately cooperative route planning of CVs, and achieve cross-domain load balancing between traffic flow and edge cloud resources domains. Furthermore, we jointly formulate route planning and resource allocation to balance the travel and service time delay by considering the mobility of CVs, distributed resources of ESs and the deadline sensitive vehicular tasks comprehensively. Besides, considering the coupled relationship between route planning and resource allocation, an alternating optimization algorithm is proposed to solve the formulated problem. we decompose it into two sub-problems. Firstly, a reinforcement learning method is used to optimize the route planning of CVs with fixed resource allocation. Then, an online learning and iterative algorithm is used to optimize the resource allocation strategy of edge cloud with fixed route selection. In order to demonstrate that our suggested scheme is more effective than other comparison schemes, a comprehensive series of experiments are conducted. [ABSTRACT FROM AUTHOR]

This paper presents a predictive computational framework for surrogate modeling of pressure field and optimization of pressure sensor placement for wind engineering applications. Firstly, a machine learning-derived surrogate model, trained by high-fidelity simulation data using finite element-based CFD and informed by a turbulence model, is developed to construct the full-field pressure from scattered sensor measurements in near real-time. Then, the surrogate pressure model is embedded in another neural network (NN) for optimizing pressure sensor placement. The goal of the NN-based optimizer is to learn the best layout of a fixed number of pressure sensors over the structural surface to deliver the most accurate full-field pressure prediction for various inflow wind conditions. We deploy the model to a representative low-rise building subjected to different wind conditions. The performance of the proposed framework is assessed by comparing the predicted results with finite element-based CFD simulation results. The framework shows excellent accuracy and efficiency, which could be potentially integrated with structural health monitoring to enable digital twins of civil structures. [ABSTRACT FROM AUTHOR]

• This study proposes an hour-ahead demand response (DR) for PV-based smart homes utilizing the AI method in digital twin while taking into account customer energy consumption and discomfort; • This study adopts a MARL approach for ensuring optimum and decentralized decision-making among several devices; • This study presents a steady cost prediction method utilizing ANN for overcoming the uncertainties in upcoming costs; • This study also compares the energy prices under 2 various scenarios including and excluding AI-enabled DR. Energy management (EM) systems need to have the flexibility to take the optimum real-time decision in the face of constantly varying market factors. Demand response (DR) has become the newest method of improving the performance and reliability of the electrical system. Here, an hour-ahead DR algorithm is proposed for EM at home. This paper presents an artificial neural network approach that uses stable cost predictions as a method for dealing with upcoming price uncertainties. For making optimum and decentralized decisions for various household devices, multi-agent reinforcement learning has been used along with predicted upcoming costs. This paper conducts the simulation with shiftable, non-shiftable, and controllable loads to determine the effectiveness of this suggested EM strategy. Based on the outcomes of the experiments, this suggested DR algorithm has capable of handling EM for a number of devices, minimizing consumer electricity expenses and discomfort prices, and helping the consumer considerably reduce its energy expenses in comparison to a benchmark using no DR. [ABSTRACT FROM AUTHOR]

Digital twins are among the most important trends of the fourth industrial revolution. They present a crucial tool for protecting critical mission systems and the development of new services, products, and processes. This paper presents the first digital twin for a data center. The rapid growth of the Internet of things and the areas of modeling and simulation results in high demand for the development of data center digital twins (DCDT) to ensure the safety/protection of critical and costly mission infrastructure and guarantee business continuity, enhance efficiency, and sustain development. This paper presents the design and implementation of a digital twin for a university data center. Different sensory data like temperature, humidity, smoke, and water leakage are analyzed using an intelligent event detection approach, which detects abnormalities using an Extreme Learning Machine (ELM) fed with the minimum ratio between successive real-time data streams. The performance of ELM has outperformed that of both Learning Vector Quantization and Radial Basis Function-based neural network classifiers and proved much faster in abnormal event detection. [ABSTRACT FROM AUTHOR]

The production of messenger ribonucleic acid (mRNA) and other biologics is performed primarily in batch mode. This results in larger equipment, cleaning/sterilization volumes, and dead times compared to any continuous approach. Consequently, production throughput is lower and capital costs are relatively high. Switching to continuous production thus reduces the production footprint and also lowers the cost of goods (COG). During process development, from the provision of clinical trial samples to the production plant, different plant sizes are usually required, operating at different operating parameters. To speed up this step, it would be optimal if only one plant with the same equipment and piping could be used for all sizes. In this study, an efficient solution to this old challenge in biologics manufacturing is demonstrated, namely the qualification and validation of a plant setup for clinical trial doses of about 1000 doses and a production scale-up of about 10 million doses. Using the current example of the Comirnaty BNT162b2 mRNA vaccine, the cost-intensive in vitro transcription was first optimized in batch so that a yield of 12 g/L mRNA was achieved, and then successfully transferred to continuous production in the segmented plug flow reactor with subsequent purification using ultra- and diafiltration, which enables the recycling of costly reactants. To realize automated process control as well as real-time product release, the use of appropriate process analytical technology is essential. This will also be used to efficiently capture the product slug so that no product loss occurs and contamination from the fill-up phase is <1%. Further work will focus on real-time release testing during a continuous operating campaign under autonomous operational control. Such efforts will enable direct industrialization in collaboration with appropriate industry partners, their regulatory affairs, and quality assurance. A production scale-operation could be directly supported and managed by data-driven decisions. [ABSTRACT FROM AUTHOR]

Machine learning (ML) is widely used in many industries, resulting in recent interests to explore ML in geotechnical engineering. Past review papers focus mainly on ML algorithms while this paper advocates an agenda to put data at the core, to develop novel algorithms that are effective for geotechnical data (existing and new), to address the needs of current practice, to exploit new opportunities from emerging technologies or to meet new needs from digital transformation, and to take advantage of current knowledge and accumulated experience. This agenda is called data-centric geotechnics and it contains three core elements: data centricity, fit for (and transform) practice, and geotechnical context. The future of machine learning in geotechnics should be envisioned with this "data first practice central" agenda in mind. Data-driven site characterization (DDSC) is an active research topic in this agenda because an understanding of the ground is crucial in all projects. Examples of DDSC challenges are ugly data and explainable site recognition. Additional challenges include making ML indispensable (ML supremacy), learning how to learn (meta-learning), and becoming smart (digital twin). [ABSTRACT FROM AUTHOR]

As heating, ventilation, and air conditioning (HVAC) systems have become one of the most contributing systems in energy consumption in the world, the control of these large-scale systems remains a challenging duty due to the decoupling effects of control variables. Accordingly, the penetration of these types of systems in all-smart buildings has increased in recent years. Furthermore, the application of digital twin as a fast-growing concept is being developed. In HVAC systems, independent and accurate control of temperature and humidity of the indoor air has been playing an undeniable role in reducing energy consumption. In this paper, to have cost-effective energy management in a single-zone HVAC system, a new reliable digital twin proximal policy optimization (PPO)–based model-independent nonsingular terminal sliding-mode control (MINTSMC) methodology has been proposed. Moreover, due to the nonlinear characteristics of HVAC systems, MINTSMC tends to handle the un-modeled system dynamics and disturbances. For regulating parameters of proposed control, an efficient PPO algorithm has been developed due to its actor-critic-based reinforcement learning. Extensive examinations and comparative analyses with particle swarm optimization designed sliding-mode control and proportional–integral–derivative controller have been made using digital twin of the proposed controller to show the importance, accuracy, and application of this method in the comfort and energy management achievement of HVAC control systems. A digital signal processor computing device has been utilized for implementation by utilizing hardware-in-loop (HIL) in the concept of the digital twin. To determine the interface between established models, software-in-loop, and HIL, the Functional Mock-up Interface has been utilized. The outcomes revealed a superior performance of suggested digital twin-based controller than the compared control methodologies in the compensation of unknown uncertainties, fast-tracking, and smooth response. [ABSTRACT FROM AUTHOR]

Circuit breakers (CBs) are mainly designed to interrupt current flow when faults are detected and have been widely used in industrial applications. The existing CBs manufacturing method is semi-automatic and requires a lot of labor. To realize flexible manufacturing, a multi-robot cooperative CBs flexible manufacturing system (CBFMS) is presented in this study. Aiming at the efficiency of the multi-robot cooperative CBFMS key units, a two-arm cooperation robot approach is proposed. The reinforcement learning algorithm is developed to optimize the manufacturing trajectory of the two-arm cooperation robot. To build and optimize the multi-robot cooperative CBFMS, a digital twin (DT) system describing all physical properties of the physical manufacturing plant is constructed for simulation. In the developed DT system, a kinematic control model of the collaboration robot is established. A real-time display of the robot's trajectory, manufacturing status, and process manufacturing is provided by the data interaction with the physical cell flow between the units. Following this design, a synchronous mapping between the flexible manufacturing DT system of the CBs and the physical workshop is realized, which enables real-time monitoring and management of the physical production line. The experiments' results show that the manufacturing efficiency, compared with traditional CBs production, is improved by 22%. Moreover, the multi-robot cooperative CBFMS can make process changes according to the production requirements, which can improve the stability of production. [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]

• A safe framework for the optimum scheduling of renewable hybrid AC-DC MGs is proposed. • A digital twin model is developed to model the complexities of the renewable energy sources. • To ensure system security, an intrusion detection system according to a sequential assumption test is presented. The current study considers numerous renewable energy resources, distributed power generation units, energy storage, and plug-in hybrid electric vehicles (PHEV) in order to propose a reliable large-scale energy management framework that can be applied to islanded and grid-connected operations of renewable hybrid AC-DC microgrids (MGs). The framework uses a bat optimization algorithm (BOA) for minimizing the operating costs of the network and in addition introduces an intrusion detection system (IDS) according to the sequential hypothesis testing (SHT) method for detecting identity-enabled cyber-attacks (i.e classification of Sybil attacks, masquerading attacks) on the wireless-enabled advanced metering infrastructures (AMI). The suggested IDS uses the received signal strength (RSS) amount for distinguishing various signal resources and detecting cyberattacks. An IEEE 33-bus testing system has been used to construct a real-time hybrid MG in order to determine the reliability and efficiency of the suggested framework. [ABSTRACT FROM AUTHOR]

The sixth-generation (6G) technology of mobile networks will establish new standards to fulfill unreachable performance requirements by fifth-generation (5G) mobile networks. This is due to the high requirements for more intelligent network, ultra-lower latency, extreme network communication speed, and supporting massive number of various connected applications. In the long term, the convergence of various business developments with communication platforms, as initiated by 5G, will exaggerate and highlight areas where 5G's capabilities will fall short of performance requirements. Motivated by the development of applications in massive connections, future networks, developments, and technological advancements for mobile communications that go beyond fifth-generation (B5G) networks are being developed. In this context, highly immersive applications are demanded, such as three-dimensional (3D) communications, digital twins, or massive extended reality (XR)/virtual reality (VR) applications, which will need 6G capabilities to be realized at scale to be commercially feasible. Mainly, we anticipate that only the upcoming 6G networks will be capable of running extremely high-performance connectivity with massive numbers of connected devices, even under laborious scenarios such as extreme density, diverse mobility, and energetic environments. In this article, we look at the most recent trends and future emerging trends that are possible to operate 6G network. Paper aims to provide more inclusive and brief review about 6G mobile communication technology in one survey paper. Initially, a comprehensive overview of the 6G system is introduced in terms of visions, drivers, requirements, architecture, and usage scenarios required to enable 6G applications. After that, the opportunities and advantages of 6G mobile technology has been discussed. Further, the promising new techniques that enable 6G technology has been highlighted. This is followed by a potential discussion of challenges and research directions. This article is envisioned to serve as an informative guideline to stimulate interest and further studies for subsequent research and development of 6G networks. Paper will enable the readers to briefly figure out the key requirements, targets, that will be need and the applications, advantages, and opportunities that can be offered as well as the challenges that need to be addressed before the implementation of this new technology. [ABSTRACT FROM AUTHOR]

Digitization offers new perspectives for educational research to identify the effects of visualizations regarding cognitive processing. In addition, new types of data can be generated, expanding the possibilities for visualizing cognitive processes and understanding human learning. Digital twins are already used in Industry 4.0, as an additional visualization to a real object, for data mining and data analysis for process optimization. The increasing integration of digital twins in the industrial sector requires the formulation of corresponding educational goals to ensure high-quality and future-oriented education. Therefore, future generations must be introduced to technologies from industry during their education. In this paper, an intelligent photometric measurement system called SmaEPho with a digital twin for science, technology, engineering, and mathematics (STEM) learning is presented. In addition to its function as a photometric measurement device, an intelligent sensor technology allows for data generation on the user's usage behavior. The digital twin reflects and visualizes these data in real-time. This enables a variety of new didactic and methodological approaches in teaching. A first study evaluating the hardware and tracking components of SmaEPho shows that the deviation accuracy of the measurement system is sufficient for experimental applications in schools. Another study with n = 52 students confirmed the excellent usability of the SmaEPho hardware platform. These research results lay the foundation for a variety of future research questions on data analysis and machine learning algorithms with the aim of increasing the quality of education. The use of intelligent digital twins as an element of digitization in educational contexts offers the extended possibility of identifying cognitive processing steps using this technology. [ABSTRACT FROM AUTHOR]