SARS-CoV-2 virus which caused the global pandemic the Coronavirus Disease-2019 (COVID-2019) has infected about 1,203,959 patients and brought forth death rate about 64,788 among 206 countries as mentioned by WHO in the month of April 2020. The clinical trials are underway for Remdesivir, an investigational anti-viral drug from Gilead Sciences. Antimalarial drugs such as Chloroquine and Hydroxychloroquine derivatives are being used in emergency cases; however, they are not suitable for patients with conditions like diabetes, hypertension and cardiac issues. The lack of availability of approved treatment for this disease calls forth the scientific community to find novel compounds with the ability to treat it. This paper evaluates the compound Andrographolide from Andrographis paniculata as a potential inhibitor of the main protease of SARS-COV-2 (Mpro) through in silico studies such as molecular docking, target analysis, toxicity prediction and ADME prediction. Andrographolide was docked successfully in the binding site of SARS-CoV-2 Mpro. Computational approaches also predicts this molecule to have good solubility, pharmacodynamics property and target accuracy. This molecule also obeys Lipinski's rule, which makes it a promising compound to pursue further biochemical and cell based assays to explore its potential for use against COVID-19. ARTICLE HISTORY

In recent years, Reversible Logic is becoming more and more prominent technology having its applications in Low Power CMOS, Quantum Computing, Nanotechnology, and Optical Computing. Reversibility plays an important role when energy efficient computations are considered. In this paper, Reversible eight-bit Parallel Binary Adder/Subtractor with Design I, Design II and Design III are proposed. In all the three design approaches, the full Adder and Subtractors are realized in a single unit as compared to only full Subtractor in the existing design. The performance analysis is verified using number reversible gates, Garbage input/outputs and Quantum Cost. It is observed that Reversible eight-bit Parallel Binary Adder/Subtractor with Design III is efficient compared to Design I, Design II and existing design.

Aluminum Hybrid Reinforcement Technology is a response to the dynamic ever-increasing service requirement of industries such as transportation, aerospace, automobile, and marine, due to its attractive properties like high ductility, highly conductivity, light weight, and high strength to weight ratio. In this evolution, an attempt has been made to investigate the wear rate of Al6061 hybrid metal matrix composite reinforced with the hard ceramic alumina (4, 8, and 12 wt.% of Al2O3) and soft solid lubricant of molybdenum disulphide (2, 4, and 6 wt.% of MoS2) is fabricated by using stir casting method. The unlubricated pins on disc wear tests were conducted to examine the wear behaviour of Al6061/12 wt.% of Al2O3/MoS2composites. The sliding wear tests were carried out at various loads of 15, 30, and 45 N, sliding velocity (1.25, 2.50, and 3.25 m/sec), and different MoS2 wt.% (2, 4, and 6 wt.%). In addition, the CNC turning experiments were conducted on Al6061/12 wt.% Al2O3/6 wt.% MoS2using CNMG 120408 uncoated carbide cutting tool under cutting of 100, 150, and 200 m/min, feed of 0.1, 0.2, and 0.3 mm/rev, and depth of cut of 1, 1.5, and 2 mm.

No abstract

A massive increase in the number of mobile devices and data hungry vehicular network applications creates a great challenge for Mobile Network Operators (MNOs) to handle huge data in cellular infrastructure. However, due to fluctuating wireless channels and high mobility of vehicular users, it is even more challenging for MNOs to deal with vehicular users within a licensed cellular spectrum. Data offloading in vehicular environment plays a significant role in offloading the vehicle's data traffic from congested cellular network's licensed spectrum to the free unlicensed WiFi spectrum with the help of Road Side Units (RSUs). In this paper, an Intelligent Reward based Data Offloading in Next Generation Vehicular Networks (IR-DON) architecture is proposed for dynamic optimization of data traffic and selection of intelligent RSU. Within IR-DON architecture, an Intelligent Access Network Discovery and Selection Function (I-ANDSF) module with Q-Learning, a reinforcement learning algorithm is designed. I-ANDSF is modeled under Software-Defined Network (SDN) controller to solve the dynamic optimization problem by performing an efficient offloading. This increases the overall system throughput by choosing an optimal and intelligent RSU in the network selection process. Simulation results have shown the accurate network traffic classification, optimal network selection, guaranteed QoS, reduced delay and higher throughput achieved by the I-ANDSF module.

No abstract

No abstract

No abstract