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Despite the availability of COVID-19 vaccines, additional more potent vaccines are still required against the emerging variations of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). In the present investigation, we have identified a promising vaccine candidate against the Omicron (B.1.1.529) using immunoinformatics approaches. Various available tools like, the Immune Epitope Database server resource, and NetCTL-1.2, have been used for the identification of the promising T-cell and B-cell epitopes. The molecular docking was performed to check the interaction of TLR-3 receptors and validated 3D model of vaccine candidate. The codon optimization was done followed by cloning using SnapGene. Finally, In-silico immune simulation profile was also checked. The identified T-cell and B-cell epitopes have been selected based on their antigenicity (VaxiJen v2.0) and, allergenicity (AllerTOP v2.0). The identified epitopes with antigenic and non-allergenic properties were fused with the specific peptide linkers. In addition, the 3D model was constructed by the PHYRE2 server and validated using ProSA-web. The validated 3D model was further docked with the Toll-like receptor 3 (TLR3) and showed good interaction with the amino acids which indicate a promising vaccine candidate against the Omicron variant of SARS-CoV-2. Finally, the codon optimization, In-silico cloning and immune simulation profile was found to be satisfactory. Overall, the designed vaccine candidate has a potential against variant of SARS-Cov-2. However, further experimental studies are required to confirm.

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The active power output of a wind power system needs regulation due to the stochastic nature of wind speed. A flywheel energy storage system (FESS) is a viable option for active power regulation in a wind power plant. An efficient energy management system (EMS) for FESS is required for healthy operation of the overall connected system. A wind speed forecasting based EMS has been proposed in this paper. It utilizes the repeated wavelet transform based ARIMA model for very short-term wind speed forecasting, which has been proven to be better than the methods that exist in the literature. An artificial neural network based model is used to translate the forecasted wind speed to instantaneous wind power output. Considering the initial energy of FESS, an optimization technique has been used to calculate the speed command to be given to FESS for a given energy exchanged with the grid. The proposed control approach extends FESS usability as an energy exchange system for a period of large change in wind speed where the normal control approach saturates the FESS speed. The feasibility of the proposed EMS algorithm has been tested in MATLAB/Simulink.

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