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In the era of digitalization and artificial intelligence (AI), the utilization of Metaverse technology has become increasingly crucial. As the world becomes more digitized, there is a pressing need to effectively transfer real-world assets into the digital realm and establish meaningful relationships between them. However, existing approaches have shown significant limitations in achieving this goal comprehensively. To address this, this research introduces an innovative methodology called the Meta-Metaverse, which aims to enhance the immersive experience and create realistic digital twins across various domains such as biology, genetics, economy, medicine, environment, gaming, digital twins, Internet of Things, artificial intelligence, machine learning, psychology, supply chain, social networking, smart manufacturing, and politics. The multi-layered structure of Metaverse platforms and digital twins allows for greater flexibility and scalability, offering valuable insights into the potential impact of advancing science, technology, and the internet. This article presents a detailed description of the proposed methodology and its applications, highlighting its potential to transform scientific research and inspire groundbreaking ideas in science, medicine, and technology. [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]

The concept of digital twinning is essential for smart manufacturing and cyber-physical systems to be connected to the Metaverse. These digital representations of physical objects can be used for real-time analysis, simulations, and predictive maintenance. A combination of smart manufacturing, Industry 4.0, and the Metaverse can lead to sustainable productivity in industries. This paper presents a practical approach to implementing digital twins of a magnetic forging holder that was designed and manufactured in this project. Thus, this paper makes two important contributions: the first contribution is the manufacturing of the holder, and the second significant contribution is the creation of its digital twin. The holder benefits from a special design and implementation, making it a user-friendly and powerful tool in materials research. More specifically, it can be employed for the thermomechanical influencing of the structure and, hence, the final properties of the materials under development. In addition, this mechanism allows us to produce a new type of creep-resistant composite material based on Fe, Al, and Y. The magnetic forging holder consolidates the powder material to form a solid state after mechanical alloying. We produce bars from the powder components using a suitable forging process in which extreme grain coarsening occurs after the final heat treatment. This is one of the conditions for achieving very high resistance to creep at high temperatures. [ABSTRACT FROM AUTHOR]

Regarding the importance of the Internet of Things (IoT) and the Metaverse as two practical emerging technologies to enhance the digitalization of public transportation systems, this article introduces an approach for the improvement of IoT and unmanned electric vehicles in the Metaverse, called the Internet of Unmanned Electric Vehicles (IoUEVs). This research includes two important contributions. The first contribution is the description of a framework for how unmanned electric vehicles can be used in the Metaverse, and the second contribution is the creation of a digital twin for an unmanned electric vehicle. In the digital twin section, which is the focus of this research, we present a digital twin of an electronic differential system (EDS) in which the stability has been improved. Robust fuzzy logic algorithm-based speed controllers are employed in the EDS to independently control the EV wheels driven by high-performance brushless DC (BLDC) electric motors. In this study, the rotor position information of the motors, which is estimated from the low-precision Hall-effect sensors mounted on the motors' shafts, is combined and converted to a set of common switching signals for empowering the EDS of the electric vehicle traction drive system. The proposed digital twin EDS relies on an accurate Hall sensor signals-based synchronizing/locking strategy with a dynamic steering pattern capable of running in severe road conditions with different surface profiles to ensure the EV's stability. Unlike recent EDSs, the proposed digital twinning approach includes a simple practical topology with no need for auxiliary infrastructures, which is able to reduce mechanical losses and stresses and can be adapted to IoUEVs more effectively. [ABSTRACT FROM AUTHOR]

Advanced mathematical and deep learning (DL) algorithms have recently played a crucial role in diagnosing medical parameters and diseases. One of these areas that need to be more focused on is dentistry. This is why creating digital twins of dental issues in the metaverse is a practical and effective technique to benefit from the immersive characteristics of this technology and adapt the real world of dentistry to the virtual world. These technologies can create virtual facilities and environments for patients, physicians, and researchers to access a variety of medical services. Experiencing an immersive interaction between doctors and patients can be another considerable advantage of these technologies, which can dramatically improve the efficiency of the healthcare system. In addition, offering these amenities through a blockchain system enhances reliability, safety, openness, and the ability to trace data exchange. It also brings about cost savings through improved efficiencies. In this paper, a digital twin of cervical vertebral maturation (CVM), which is a critical factor in a wide range of dental surgery, within a blockchain-based metaverse platform is designed and implemented. A DL method has been used to create an automated diagnosis process for the upcoming CVM images in the proposed platform. This method includes MobileNetV2, a mobile architecture that improves the performance of mobile models in multiple tasks and benchmarks. The proposed technique of digital twinning is simple, fast, and suitable for physicians and medical specialists, as well as for adapting to the Internet of Medical Things (IoMT) due to its low latency and computing costs. One of the important contributions of the current study is to use of DL-based computer vision as a real-time measurement method so that the proposed digital twin does not require additional sensors. Furthermore, a comprehensive conceptual framework for creating digital twins of CVM based on MobileNetV2 within a blockchain ecosystem has been designed and implemented, showing the applicability and suitability of the introduced approach. The high performance of the proposed model on a collected small dataset demonstrates that low-cost deep learning can be used for diagnosis, anomaly detection, better design, and many more applications of the upcoming digital representations. In addition, this study shows how digital twins can be performed and developed for dental issues with the lowest hardware infrastructures, reducing the costs of diagnosis and treatment for patients. [ABSTRACT FROM AUTHOR]

Global concerns regarding environmental preservation and energy sustainability have emerged due to the various impacts of constantly increasing energy demands and climate changes. With advancements in smart grid, edge computing, and Metaverse-based technologies, it has become apparent that conventional private power networks are insufficient to meet the demanding requirements of industrial applications. The unique capabilities of 5G, such as numerous connections, high reliability, low latency, and large bandwidth, make it an excellent choice for smart grid services. The 5G network industry will heavily rely on the Internet of Things (IoT) to progress, which will act as a catalyst for the development of the future smart grid. This comprehensive platform will not only include communication infrastructure for smart grid edge computing, but also Metaverse platforms. Therefore, optimizing the IoT is crucial to achieve a sustainable edge computing network. This paper presents the design, fabrication, and evaluation of a super-efficient GSM triplexer for 5G-enabled IoT in sustainable smart grid edge computing and the Metaverse. This component is intended to operate at 0.815/1.58/2.65 GHz for 5G applications. The physical layout of our triplexer is new, and it is presented for the first time in this work. The overall size of our triplexer is only 0.007 λg2, which is the smallest compared to the previous works. The proposed triplexer has very low insertion losses of 0.12 dB, 0.09 dB, and 0.42 dB at the first, second, and third channels, respectively. We achieved the minimum insertion losses compared to previous triplexers. Additionally, the common port return losses (RLs) were better than 26 dB at all channels. [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]

Symmetry is essential in the design of complex systems like the Metaverse Transportation Sys-tem (MTS) and helps ensure that all components work together effectively. In the development of a microstrip diplexer for 5G-enabled IoT and MTS, maintaining symmetry is crucial to achieving flat responses with low group delays. By integrating transportation technology and the Metaverse, the Metaverse Transportation System (MTS) can greatly improve the effectiveness and intellect of transportation systems in reality. To establish a dependable network, it is essential to include 5G-enabled Internet of Things (IoT) and sensor networks with a sustainable design that focuses on efficiency and energy conservation. A three-channel microstrip lowpass-bandpass diplexer has been developed for 5G-enabled IoT and MTS implementation. Multi-channel designs are rare due to the complex design process, but this diplexer is very compact at only 0.004 λg2. All channels have flat responses with group delays of 0.34 ns, 1.7 ns, and 0.34 ns at the lower, middle, and upper passbands, respectively. The lowpass channel has a cut-off frequency of 1.22 GHz, suitable for mid-band 5G applications. Compared to previous work, this diplexer achieves the smallest size, lowest group delay, and insertion and return losses at the lower channel. It consists of a lowpass-bandpass section connected to a band-pass filter analyzed mathematically, and its performance has been verified through simulation and measurement with good accuracy. [ABSTRACT FROM AUTHOR]

Although the Metaverse is becoming a popular technology in many aspects of our lives, there are some drawbacks to its implementation on clouds, including long latency, security concerns, and centralized infrastructures. Therefore, designing scalable Metaverse platforms on the edge layer can be a practical solution. Nevertheless, the realization of these edge-powered Metaverse ecosystems without high-performance intelligent edge devices is almost impossible. Neuromorphic engineering, which employs brain-inspired cognitive architectures to implement neuromorphic chips and Tiny Machine Learning (TinyML) technologies, can be an effective tool to enhance edge devices in such emerging ecosystems. Thus, a super-efficient TinyML processor to use in the edge-enabled Metaverse platforms has been designed and evaluated in this research. This processor includes a Winner-Take-All (WTA) circuit that was implemented via a simplified Leaky Integrate and Fire (LIF) neuron on an FPGA. The WTA architecture is a computational principle in a neuromorphic system inspired by the mini-column structure in the human brain. The resource consumption of the WTA architecture is reduced by employing our simplified LIF neuron, making it suitable for the proposed edge devices. The results have indicated that the proposed neuron improves the response speed to almost 39% and reduces resource consumption by 50% compared to recent works. Using our simplified neuron, up to 4200 neurons can be deployed on VIRTEX 6 devices. The maximum operating frequency of the proposed neuron and our spiking WTA is 576.319 MHz and 514.095 MHz, respectively. [ABSTRACT FROM AUTHOR]

Digital twins are the most important parts of Cyber-Physical Systems (CPSs), and play a crucial role in the realization of the Metaverse. Therefore, two important factors: flexibility and adaptability, need to be focused on digital twinning systems. From a virtual perspective, constructing buildings, structures, and mechanisms in the Metaverse requires digital materials and components. Hence, accurate and reliable digital models can guarantee the success of implementation, particularly when it comes to completing physical twins in the real world. Accordingly, four Machine Learning (ML) methods to make digital twins of an advanced 42SiCr alloy considering all of its uncertainties and non-linearities have been employed in this paper. These ML methods accelerate the digitalization of the proposed alloy and allow users to employ them for a wide range of similar metals. Based on this technique, producers can borrow these virtual materials and build their structures in the Metaverse. This way, if the properties of the materials were satisfactory, they might buy them and start manufacturing their products. As a case study, we focus on digital twining of an 42SiCr steel with some influential factors in its mechanical properties, making the nature of the alloy complex. Processes, including heat treatment, may restore the material's deformability; however, Quenching and Partitioning (Q&P) not only eliminates the impact of cold forming but also provides advanced high-strength steel (AHSS) properties. In this research, the combined impacts of different Q&P treatments were investigated on the mechanical properties of 42SiCr steel alloy. The results have shown the acceptability and accuracy of the proposed ML methods in realizing the digital twins of this complex alloy. [ABSTRACT FROM AUTHOR]

Preparing the infrastructure for analyzing, recognizing, and characterizing microorganisms in the Metaverse can transform the fields of biology, medicine, and drug discovery. Accordingly, the realization of digital twins of microorganisms, such as viruses, fungi, algae, bacteria, protozoa, archaea, and multicellular animal parasites, can streamline the applicability of the Metaverse and similar emerging technologies like Cyber-Physical Healthcare Systems (CPHS). This is why a new approach to the digital twinning of bacteria has been presented in this research. This method of digital twinning can revolutionize the research and study of bacteria because it allows us to separate useful and harmful bacterial species, increasing the efficiency of treatment noticeably. This innovative method can easily be used by clinics, medical centers, or even by private users physically and virtually and can be adapted to every centralized or decentralized Metaverse Platform. To determine the proper treatment, biologists have always tried to identify the correct bacterial species that prompted a bacterial infection. They use various indicators, such as the bacterial cell's shape and the size of the colony formed by the bacteria, to classify different types of bacteria with different biochemistries and shapes. However, it is challenging because of the extensive similarities between some species. For instance, such similarities exist between Staphylococcus aureus and Staphylococcus saprophyticus, which has caused numerous false diagnostic reports by operators. Wrong species reports bring about treatment failure and increase antibiotic resistance issues. Therefore, the digital twins of bacteria cover all of their identifiable characteristics and overcome many limitations to study them. In this approach, DTL techniques, including MobileNetV2, EfficientNetV2-S, and ResNet-50, have been employed to build digital twins of bacteria species. A hybrid dataset was used for training and evaluation. Among the models, EfficientNetV2-S exhibited the best performance, with a validation accuracy of 99.58% and a test accuracy of 99.33%. The results showed the ability of deep learning models to make bacteria digital twins based on image processing from different labs, thus assisting experts in speeding up the process and reducing diagnostic errors. In addition, in the mispredicted cases, the correct species was among the first three choices of the model. Therefore, not only can experts use DTL approaches to speed up the digital twin realization of microorganisms in the Metaverse, but they can also use these methods to reduce diagnostic errors. • Introducing a technique to streamline digital twinning of microorganisms using CNN. • Describing machine learning digital twins of microorganisms in the Metaverse. • Presenting an exact, economical digital twinning approach via smartphone photography. • Providing a comprehensive bacteria species dataset for future method development. [ABSTRACT FROM AUTHOR]