Dual_Pachi: Attention-based dual path framework with intermediate second order-pooling for Covid-19 detection from chest X-ray images.

Numerous machine learning and image processing algorithms, most recently deep learning, allow the recognition and classification of COVID-19 disease in medical images. However, feature extraction, or the semantic gap between low-level visual information collected by imaging modalities and high-level semantics, is the fundamental shortcoming of these techniques. On the other hand, several techniques focused on the first-order feature extraction of the chest X-Ray thus making the employed models less accurate and robust. This study presents Dual_Pachi: Attention Based Dual Path Framework with Intermediate Second Order-Pooling for more accurate and robust Chest X-ray feature extraction for Covid-19 detection. Dual_Pachi consists of 4 main building Blocks; Block one converts the received chest X-Ray image to CIE LAB coordinates (L & AB channels which are separated at the first three layers of a modified Inception V3 Architecture.). Block two further exploit the global features extracted from block one via a global second-order pooling while block three focuses on the low-level visual information and the high-level semantics of Chest X-ray image features using a multi-head self-attention and an MLP Layer without sacrificing performance. Finally, the fourth block is the classification block where classification is done using fully connected layers and SoftMax activation. Dual_Pachi is designed and trained in an end-to-end manner. According to the results, Dual_Pachi outperforms traditional deep learning models and other state-of-the-art approaches described in the literature with an accuracy of 0.96656 (Data_A) and 0.97867 (Data_B) for the Dual_Pachi approach and an accuracy of 0.95987 (Data_A) and 0.968 (Data_B) for the Dual_Pachi without attention block model. A Grad-CAM-based visualization is also built to highlight where the applied attention mechanism is concentrated.

Current Sheet Antenna Array and 5G: Challenges, Recent Trends, Developments, and Future Directions.

Designing an ultra-wideband array antenna for fifth generation (5G) is challenging for the antenna designing community because of the highly fragmented electromagnetic spectrum. To overcome bandwidth limitations, several millimeter-wave bands for 5G and beyond applications are considered; as a result, many antenna arrays have been proposed during the past decades. This paper aims to explore recent developments and techniques regarding a specific type of phased array antenna used in 5G applications, called current sheet array (CSA). CSA consists of capacitively coupled elements placed over a ground plane, with mutual coupling intentionally introduced in a controlled manner between the elements. CSA concept evolved and led to the realization of new array antennas with multiple octaves of bandwidth. In this review article, we provide a comprehensive overview of the existing works in this line of research. We analyze and discuss various aspects of the proposed array antennas with the wideband and wide-scan operation. Additionally, we discuss the significance of the phased array antenna in 5G communication. Moreover, we describe the current research challenges and future directions for CSA-based phased array antennas.

Potential Inhibitors of SARS-CoV-2 from Neocarya macrophylla (Sabine) Prance ex F. White: Chemoinformatic and Molecular Modeling Studies for Three Key Targets

Objectives: The novel coronavirus disease-2019 (COVID-19) that emerged in China, is a highly transmittable and pathogenic viral infection caused by the severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2); the disease has been declared by the World Health Organization as a Public Health Emergency of International Concern. The unavailability of approved therapeutic agents or vaccines is of great concern. This study performed molecular docking and absorption, distribution, metabolism, excretion and toxicity (ADMET) analysis of some compounds isolated from Neocarya macrophylla (Sabine) Prance ex F. White (Chrysobalanaceae) against three targets of SARS-CoV-2 proteins (3C-like protease, spike protein, and papain-like protease). Materials and Methods: Phytoconstituents isolated from N. macrophylla were screened against key targets of SARS-CoV-2 using Auto Dock Vina, while the ADMET analysis was performed using swiss ADME and pkCSM ADMET descriptors algorithm protocols. Results: The in silico computational studies revealed that the compounds (catechin, catechin-3-rhamnoside, quercetin, and epicatechin) isolated from N. macrophylla can effectively bind with high affinity and lower energy values to the three target proteins of SARS-CoV-2. ADMET analysis was used to predict important pharmacokinetic properties of the compounds, such as aqueous solubility, blood-brain barrier, plasma protein binding, CYP2D6 binding, intestinal absorption, and hepatotoxicity. Conclusion: The findings of this study have shown that N. macrophylla contains potential leads for SARS-CoV-2 inhibition and thus, should be studied further for development as therapeutic agents against COVID-19.