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SARS-CoV-2 pandemic has recently made the entire world come to a standstill. The number of cases in the world, especially India, have been increasing exponentially. The need of the hour is to assimilate as much data as possible to fast track the pipeline of bringing in new therapeutic tools against this fatal virus. In this brief communication, we aim to throw light on the various variants of the proteins involved heavily in the pathophysiology of COVID-19, namely Spike protein, ACE2, GRP78, TMPRSS2 and NSP-12. We also portray the molecular docking studies of these proteins with specific drugs that are currently being associated with the same. In our brief study, we come across a few key findings. First of all the combinations of the variants of spike protein and ACE2 binding show overall 25% unfavourable ΔΔG. Second, NSP12 is the most mutation prone among all the NSPs of the SARS-CoV-2 genome and the most common mutations are P323L and A97V. Third, we discovered the variants found in the Indian subpopulation that have greater binding with the currently investigated drugs.

Abstract-Memristors, initially introduced in the 1970s, have received increased attention upon successful synthesis in 2008. Considerable work has been done on its modeling and applications in specific areas, however, very little is known on the potential of memristors for control applications. Being nanoscopic variable resistors, one can consider its use in making variable gain amplifiers which in turn can implement gain scheduled control algorithms. The main contribution of this paper is the development of a generic memristive analog gain control framework and theoretic foundation of a gain scheduled robustadaptive control strategy which can be implemented using this framework. Analog memristive controllers may find applications in control of large array of miniaturized devices where robust and adaptive control is needed due to parameter uncertainty and ageing issues.

In this paper we propose a new array sorting algorithm with average and best time complexity of O(n). Its best, worst and average time complexity has been analysed. Also the difficulty of applying this algorithm with strings has been discussed and its solution too is found. The limitation of the solution is also analysed.

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The three-tier spectrum sharing framework (3-TSF) is a spectrum sharing model adopted by the Federal Communications Commission. According to this model, under-utilized federal spectrum like the Citizens Broadband Radio Service band is released for shared use where the highest preference is given to Tier-1 followed by Tier-2 (T2) and then Tier-3 (T3). In this paper, we study how a wireless operator, who is interested in maximizing its profit, can strategically operate as a T2 and/or a T3 user. T2 is characterized by paid but "almost" guaranteed and interference-free channel access while T3 access is free but has the lesser guarantee and also faces channel interference. So the operator has to optimally decide between paid but better channel quality and free but uncertain channel quality. Also, the operator has to make these decisions without knowing future market variables like customer demand or channel availability. The main contribution of this paper is a deterministic online algorithm for leasing channels that has finite competitive ratio, low time complexity, and that does not rely on the knowledge of market statistics. Such algorithms are desirable in the early stages of the deployment of 3-TSF because the knowledge of market statistics may be rather inaccurate. We use tools from the ski-rental literature to design the online algorithm. The online optimization problem for leasing channels is a novel generalization of the skirental problem. We, therefore, make fundamental contributions to the ski-rental literature, the applications of which extend beyond this paper. We also conduct simulations using synthetic traces to compare our online algorithm with the benchmark and state-of-the-art algorithms.