Over the years the global economy, including India, has undergone a profound change and observed a pioneer role of innovation and technology in every sector. Rapid technological dissemination has seen human or physical touch points being replaced by digital interface. With digitization happening everywhere, it becomes all the more important for the education sector to adapt it in order to keep pace with the challenging world. With the onset of the global pandemic of Covid-19, the only option left with the education sector is to switch to online classes for its smooth and continuous working. This research aims to investigate the challenges that are faced by the students and their level of satisfaction from these classes. The study is based on primary data collected from 700 students of various colleges across Universities in India. The study employs regression models, crosstabs, and various other descriptive statistics for analysis of the data. The findings of the study suggest that the students face a lot of glitches while learning from the online mode of education and would need time to adjust. The research also provides an insight on the scope and the future of online education.

The present paper determines the repercussions of the coronavirus on the Indian financial markets by taking the eight sectoral indices into account. By taking the sectoral indices into account, the study deduces the impact of virus outbreak on the various sectoral indices of the Indian stock market. Employing Welch's t-test and Non-parametric Mann-Whitney U test, we empirically analysed the daily returns of eight sectoral indices: Nifty Auto, Nifty FMCG, Nifty IT, Nifty Media, Nifty Metal, Nifty Oil and Gas, Nifty Pharma, and Nifty Bank. The results unveiled that pandemic had a negative impact on the automobile, FMCG, pharmaceuticals, and oil and gas sectors in the short run. In the long run, automobile, oil and gas, metals, and the banking sector have suffered enormously. The results further unveiled that no selected indices underperformed the domestic average, except NIFTY Auto.

<p class="0abstract"><span lang="EN-IN">The outburst of the pandemic COVID-19 in 2019 from the land of China to the entire world has brought a major change in people's living style. The severity of the disease is so much that more than 20 million people are affected by it worldwide. The recommended preventive measure to avoid the spread of this fatal disease is to maintain social distance leading to a significant economic disruption that could submerge the economies globally. However in order to keep the economies moving, the work from culture is adopted globally wherein the employees are connected via the internet and the work continues to happen. This paper aims to discover how people adopt such an attempt in the working culture. Also, the purpose of the paper is to find out its bearing on the employees' working capacity and whether they would like to continue with this arrangement in the future. The research employs primary data collected from 400 employees across various industries who are currently working from home. The research proposes advantages and challenges in working from home with a majority of employees preferring the method. </span></p>

MANET (Mobile ad-hoc network) is a collection of mobile nodes that communicate over wireless links without having any pre-existing fixed underlying infrastructure. Their dynamically changing topology, lack of central administration and resource constraints makes them prone to security attacks. Quantifying security has always been a difficult task as there is no fixed measure of how secure is secure enough. Moreover, different people have different interpretations for security. In this paper, a security measurement framework for MANETS based on the five parameters CIANA (Confidentiality, Integrity, Authentication, Non-repudiation and Authorization) has been proposed. AHP (Analytic Hierarchy Process) and TOPSIS (Technique for order preference by similarity to Ideal solution) are then applied on the proposed models to select the most secure network amongst a set of heterogeneous networks.

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