Corona Virus or COVID-19 first appeared in December, 2019 in Wuhan, China. People tweeted aggressively on twitter at that time. This paper analysed the tweets regarding COVID-19 from November, 2019 to May, 2020 in India and its affect. All tweets are categorized into 3 categories(Positive, Negative and Neutral). Multiple datasets are created Statewise, Month-wise, combined of all states to analyze the people's reactions towards Lockdown in June, 2020 and about everything related to COVID-19. Most people started having Negative tweets but with increasing time people shifted towards positive and neutral comments. In April, 2020 most comments were Positive and about winning against Corona virus.

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Nowadays, localization of mobile node using wireless local area network (WLAN) become a most emerging area. Its location based services leads to make it applicable in various fields like intruder identification, healthcare system, navigation etc. To determine the location of a mobile node various localization techniques have been proposed. In this paper, we propose a new range-free localization algorithm based on K-nearest neighbors (K-NN) method for IEEE802.11 WLAN. Our algorithm finds K-NN by comparing received signal strength (RSS) of mobile node (MN). Thereafter, location of MN is computed by calculating weighted average of coordinates of K-NN. We also compared our proposed algorithm with RSS difference and conventional K-NN methods. Extensive simulation results show that the accuracy of our algorithm is better than these two methods. We also examine the effect of network size, grid size, number of access point (AP) and shadowing factor on localization accuracy of proposed algorithm.

We test the generalized scalar-tensor theory in static systems, namely galaxy clusters. The Degenerate higher-order scalar-tensor (DHOST) theory modifies the Newtonian potential through effective Newtonian constant and Ξ1 parameter in the small scale, which modifies the hydrostatic equilibrium. We utilize the well-compiled X-COP catalog consisting of 12 clusters with Intra Cluster Medium (ICM) pressure profile by Sunyaev-Zel'dovich effect data and temperature profile by X-ray data for each cluster. We perform a fully Bayesian analysis modeling Navarro-Frenk-White (NFW) for the mass profile and the simplified Vikhlinin model for the electron density. Carefully selecting suitable clusters to present our results, we find a mild to moderate, i.e, ∼ 2σ significance for a deviation from the standard scenario in 4 of the clusters. However, in terms of Bayesian evidence, we find either equivalent or mild preference for GR. We estimate a joint constraint of Ξ1 = −0.030 ± 0.043 using 8 clusters, for a modification from a ΛCDM scenario. This limit is in very good agreement with theoretical ones and an order of magnitude more stringent than the previous constraint obtained using clusters. We also quote a more conservative limit of Ξ1 = −0.061±0.074. Finally, we comment on the tentative redshift dependence (Ξ1(z)), finding a mild preference ( 2σ) for the same.