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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.

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.

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 evaluatio

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.

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.

The global spread of COVID-19 highlights the urgency of quickly finding drugs and vaccines and suggests that similar challenges will arise in the future. This underscores the need for ongoing efforts to overcome the obstacles involved in the development of potential treatments. Although some progress has been made in the use of Artificial Intelligence (AI) in drug discovery, virologists, pharmaceutical companies, and investors seek more long-term solutions and greater investment in emerging technologies. One potential solution to aid in the drug-development process is to combine the capabilities of the Internet of Medical Things (IoMT), edge computing (EC), and deep learning (DL). Some practical frameworks and techniques utilizing EC, IoMT, and DL have been proposed for the monitoring and tracking of infected individuals or high-risk areas. However, these technologies have not been widely utilized in drug clinical trials. Given the time-consuming nature of traditional drug- and vaccine-development methods, there is a need for a new AI-based platform that can revolutionize the industry. One approach involves utilizing smartphones equipped with medical sensors to collect and transmit real-time physiological and healthcare information on clinical-trial participants to the nearest edge nodes (EN). This allows the verification of a vast amount of medical data for a large number of individuals in a short time frame, without the restrictions of latency, bandwidth, or security constraints. The collected information can be monitored by physicians and researchers to assess a vaccine’s performance.

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.

This paper introduces Tensor-Organized Memory (TOM), a novel neuromorphic architecture inspired by the human brain’s structural and functional principles. Utilizing spike-timing-dependent plasticity (STDP) and Hebbian rules, TOM exhibits cognitive behaviors similar to the human brain. Compared to conventional architectures using a simplified leaky integrate-and-fire (LIF) neuron model, TOM showcases robust performance, even in noisy conditions. TOM’s adaptability and unique organizational structure, rooted in the Columnar-Organized Memory (COM) framework, position it as a transformative digital memory processing solution. Innovative neural architecture, advanced recognition mechanisms, and integration of synaptic plasticity rules enhance TOM’s cognitive capabilities. We have compared the TOM architecture with a conventional floating-point architecture, using a simplified LIF neuron model. We also implemented tests with varying noise levels and partially erased messages to evaluate its robustness. Despite the slight degradation in performance with noisy messages beyond 30%, the TOM architecture exhibited appreciable performance under less-than-ideal conditions. This exploration into the TOM architecture reveals its potential as a framework for future neuromorphic systems. This study lays the groundwork for future applications in implementing neuromorphic chips for high-performance intelligent edge devices, thereby revolutionizing industries and enhancing user experiences within the power of artificial intelligence.

One of the most critical challenges in managing complex diseases like COVID-19 is to establish an intelligent triage system that can optimize the clinical decision-making at the time of a global pandemic. The clinical presentation and patients’ characteristics are usually utilized to identify those patients who need more critical care. However, the clinical evidence shows an unmet need to determine more accurate and optimal clinical biomarkers to triage patients under a condition like the COVID-19 crisis. Here we have presented a machine learning approach to find a group of clinical indicators from the blood tests of a set of COVID-19 patients that are predictive of poor prognosis and morbidity. Our approach consists of two interconnected schemes: Feature Selection and Prognosis Classification. The former is based on different Matrix Factorization (MF)-based methods, and the latter is performed using Random Forest algorithm. Our model reveals that Arterial Blood Gas (ABG) O₂ Saturation and C-Reactive Protein (CRP) are the most important clinical biomarkers determining the poor prognosis in these patients. Our approach paves the path of building quantitative and optimized clinical management systems for COVID-19 and similar diseases.

A number of binuclear and mononuclear macroacyclic Schiff base complexes containing piperazine moieties were synthesized. The binuclear complexes (M2L1) characterized by elemental analysis, IR and, also Ni(II) complex by a X-ray single crystal structural analysis. In this case, X-ray showed that each nickel atom is in a mer-N3O3 octahedral coordination environment. Moreover, mononuclear macroacyclic Schiff base complexes (ML2 and ML3) were prepared based on the condensation reaction of an amine containing piperazine moiety and 2-hydroxy banzaldehyde or formyl pyridine in 1:2 mol ratio in the presence of Ni(II), Cu(II), and Co(II) ions. All of compounds were characterized by elemental analyses, FT-IR, mass spectrometry and ligands characterized by 1H and 13C NMR spectroscopy. The cytotoxicity of the complexes was evaluated against two different cancer cell lines including MCF-7 (breast) and A549 (lung) adenocarcinoma cells. In general, binuclear complexes, possesses a higher cytotoxic effect against the tested cells than the other complexes. In addition, the biological assessment of complexes have been examined via molecular docking. As an interesting results, the binuclear complexes have the highest inhibition effect against cytotoxic receptors.