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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. [ABSTRACT FROM AUTHOR]

This study aims to predict vital soil physical properties, including clay, sand, and silt, which are essential for agricultural management and environmental protection. Precision distribution of soil texture is crucial for effective land resource management and precision agriculture. To achieve this, we propose an innovative approach that combines Geospatial Artificial Intelligence (GeoAI) with the fusion of satellite imagery to predict soil physical properties. We collected 317 soil samples from Iran's Golestan province for dependent data. The independent dataset encompasses 14 parameters from Landsat-8 satellite images, seven topographic parameters from the Shuttle Radar Topography Mission (SRTM) DEM, and two meteorological parameters. Using the Random Forest (RF) algorithm, we conducted feature importance analysis. We employed a Convolutional Neural Network (CNN), RF, and our hybrid CNN-RF model to predict soil properties, comparing their performance with various metrics. This hybrid CNN-RF network combines the strengths of CNN networks and the RF algorithm for improved soil texture prediction. The hybrid CNN-RF model demonstrated superior performance across metrics, excelling in predicting sand (MSE: 0.00003%, RMSE: 0.006%), silt (MSE: 0.00004%, RMSE: 0.006%), and clay (MSE: 0.00005%, RMSE: 0.007%). Moreover, the hybrid model exhibited improved precision in predicting clay (R2: 0.995), sand (R2: 0.992), and silt (R2: 0.987), as indicated by the R2 index. The RF algorithm identified MRVBF, LST, and B7 as the most influential parameters for clay, sand, and silt prediction, respectively, underscoring the significance of remote sensing, topography, and climate. Our integrated GeoAI-satellite imagery approach provides valuable tools for monitoring soil degradation, optimizing agricultural irrigation, and assessing soil quality. This methodology has significant potential to advance precision agriculture and land resource management practices. [ABSTRACT FROM AUTHOR]

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]

This paper introduces a novel algorithm for designing a low-pass filter (LPF) and a microstrip Wilkinson power divider (WPD) using a neural network surrogate model. The proposed algorithm is applicable to various microwave devices, enhancing their performance and frequency response. Desirable output parameters can be achieved for the designed LPF and WPD by using the proposed algorithm. The proposed artificial neural network (ANN) surrogate model is employed to calculate the dimensions of the LPF and WPD, resulting in their efficient design. The LPF and WPD designs incorporate open stubs, stepped impedances, triangular-shaped resonators, and meandered lines to achieve optimal performance. The compact LPF occupies a size of only 0.15 λg × 0.081 λg, and exhibits a sharp response within the transmission band, with a sharpness parameter of approximately 185 dB/GHz. The designed WPD, operating at 1.5 GHz, exhibits outstanding harmonics suppression from 2 GHz to 20 GHz, with attenuation levels exceeding 20 dB. The WPD successfully suppresses 12 unwanted harmonics (2nd to 13th). The obtained results demonstrate that the proposed design algorithm effectively accomplishes the LPF and WPD designs, exhibiting desirable parameters such as operating frequency and high-frequency harmonics suppression. The WPD demonstrates a low insertion loss of 0.1 dB (S21 = 0.1 dB), input and output return losses exceeding 30 dB (S11 = −35 dB, S22 = −30 dB), and an output ports isolation of more than 32 dB (S23 = −32 dB), making it suitable for integration into modern communication systems. [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]

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]

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]

Background & Objective: Acute myocardial infarction (MI) is caused due to coronary artery occlusion and divided into two forms of ST-elevation (STEMI) and non-ST-elevation (NSTEMI) myocardial infarction. This study aimed to determine the success rate of treatment with primary PCI (percutaneous coronary intervention) versus thrombolysis in the establishment of perfusion and to evaluate the short-term complications caused by each method. Materials & Methods: In this prospective cohort study, 90 patients were selected for primary PCI treatment and 90 patients for thrombolytic treatment. Clinical data and basic demographic characteristics of the patients and cardiovascular risk factors were recorded. The success rate of coronary reperfusion by primary PCI and thrombolytic therapy was then determined by angiography and 90-minute ECG with resolution or improvement of chest pain. All patients were examined for any serious complications such as cerebrovascular accidents, shock, and heart failure for 30 days after treatment. Data were analyzed using SPSS ver. 20. Results: In the primary PCI group, 75.6% of the patients were males, and 24.4% were females, but in the thrombolytic group, 76.6% and 23.3% of the cases were males and females, respectively. The mean door-to-balloon time for PCI was 63.60 ± 23.92 and was 53.70 ±21.52 min in the thrombolytic group. Thrombolysis in myocardial infarction (TIMI) flow grade III and TIMI-II were the most frequent in primary PCI and thrombolytic groups, respectively. The odds ratio (OD) of mechanical MI and major arrhythmia in patients received the primary PCI compared to those received thrombolytic therapy was 0.24 and 0.66, respectively. Also, the OD of mortality in the primary PCI group was 2.12. Conclusion: Our findings suggest that in STEMI patients, the chances of short term complications such as post-MI mechanical complications, major arrhythmia, and bleeding requiring blood transfusion were lower in patients who were treated with primary PCI than those who received thrombolytic treatment. Also, the average hospitalization in CCU and hospital in the primary PCI group was lower than the thrombolytic group. [ABSTRACT FROM AUTHOR]

Background and Objectives: Some studies have shown that spirituality and religion are related to more life satisfaction and less depression. The present study examined the effectiveness of Islamic cognitive-behavioral therapy (CBT) compared to conventional CBT in reducing depression and increasing life satisfaction and spiritual health in patients with depression. Methods: This is a random trial with a pre-test-post-test design. There were 30 participants in this study; 15 participants received conventional CBT and 15 received Islamic CBT. The instruments used for data collection included a semi-structured interview based on diagnostic and statistical manual of mental disorders, 5th edition (DSM-5), Glock and Stark's (1970) Religiosity Scale, Beck's depression inventory, Diner's life satisfaction questionnaire (1985), and Paloutzian's spiritual wellbeing scale, which were filled in before and after the treatment sessions. Each protocol included ten 50-minute sessions. The collected data were finally analyzed using multivariate analysis of covariance (MANCOVA). Results: Islamic CBT led to a significantly higher level of spiritual health compared to conventional CBT (P<0.001). Both methods led to a significant reduction in depression and increased life satisfaction and no significant difference was found between them. Conclusion: It is suggested that Islamic CBT should be improved in terms of psychological components and techniques combined with spirituality so that its effectiveness in reducing depression and increasing life satisfaction can be improved compared to conventional CBT. [ABSTRACT FROM AUTHOR]

The Covid 19 virus pandemic (Corona) has not only affected public health, but has caused a major recession in the world. In addition to the consequences of the Corona virus, the Iranian economy is also facing the pressure of sanctions and is experiencing these two factors simultaneously. More importantly, businesses have not been immune to the virus. Kermanshah province with high unemployment is at risk of damage to rural businesses. Therefore, the present study seeks to provide a model for the revitalization of small and medium-sized rural businesses affected by the Covid 19 crisis, which has been conducted in rural businesses in Kermanshah province. Experts of the Jihad Agricultural Organization of the province and professors of Razi University have formed the statistical population of this study who were selected by purposive sampling and snowball method. Semistructured interview method was used to collect research data in the qualitative part and survey method was used in the quantitative part. The data collection process continued until the theoretical saturation of the available information Qualitative analysis of the data was performed by Max Kyoda software and by analyzing 15 interviews, a total of 45 codes were identified. The data were analyzed in four main categories: financial - facility, service - support, business empowerment and market summary. The two factors of finance - facilities and services - support empower the business, and this in turn can be a factor in creating decisions with a focus on customer, market and marketing. In the quantitative part, the results of Friedman prioritization test showed that for the rehabilitation of damaged rural units in Kermanshah province, respectively, financial-facility factors (3.43), empowermentresilience (2.73), support-services (2.40) and market factors (1.43) had a higher priority. [ABSTRACT FROM AUTHOR]