With almost one third of the world on a lockdown, the corporates and the offices have now rapidly shifted to working from home. Since no specific treatment has been suggested by any medical institution so far, World Health Organization has recommended that the only possible solution to be safe is to self-isolate and stay home. Due to this, the world has come to a screeching halt and the businesses have to be shifted to remote work. Work-from-Home is a very new experience for most of us and hence the perception of the people ranges from being very excited to very hopeless. This study aims to examine the sentiments of the people regarding Work-from-Home concept by analysing twitter activities posted on social media. Total 100,000 tweets were analysed for this study. Results indicate that Work-from-Home concept was taken positively by the people. The emotions associated with most of the tweets were of trust and anticipation indicating that this concept is being welcomed by the people.

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Availability of natural fibre, low cost and ease of manufacturing have urged the attention of researchers towards the possibility of reinforcement of natural fibre to improve their mechanical properties and study the extent to which they satisfy the required specifications of good reinforced polymer composite for industrial and structural applications. The chemically treated natural fibre shows better improvement in properties than untreated fibres. The chemically treated natural fibre has improved interfacial adhesion between fibre surface and polymer matrix. Natural fibre reinforcements have shown better results in impact toughness and fatigue strength. This review aims at explaining about the research and development in the improvement in properties of natural fibre reinforced polymer composites along with its application.

Background With increasing numbers of patients with COVID-19 globally, China and the World Health Organization have been blamed by some for the spread of this disease. Consequently, instances of racism and hateful acts have been reported around the world. When US President Donald Trump used the term “Chinese Virus,” this issue gained momentum, and ethnic Asians are now being targeted. The online situation looks similar, with increases in hateful comments and posts. Objective The aim of this paper is to analyze the increasing instances of cyber racism during the COVID-19 pandemic, by assessing emotions and sentiments associated with tweets on Twitter. Methods In total, 16,000 tweets from April 11-16, 2020, were analyzed to determine their associated sentiments and emotions. Statistical analysis was carried out using R. Twitter API and the sentimentr package were used to collect tweets and then evaluate their sentiments, respectively. This research analyzed the emotions and sentiments associated with terms like “Chinese Virus,” “Wuhan Virus,” and “Chinese Corona Virus.” Results The results suggest that the majority of the analyzed tweets were of negative sentiment and carried emotions of fear, sadness, anger, and disgust. There was a high usage of slurs and profane words. In addition, terms like “China Lied People Died,” “Wuhan Health Organization,” “Kung Flu,” “China Must Pay,” and “CCP is Terrorist” were frequently used in these tweets. Conclusions This study provides insight into the rise in cyber racism seen on Twitter. Based on the findings, it can be concluded that a substantial number of users are tweeting with mostly negative sentiments toward ethnic Asians, China, and the World Health Organization.

Rainfall forecasting plays an important role in catchment management applications, the flood warning system being one of them. Rainfall forecasting is one of the most difficult tasks given the variability of space, time and other given conditions change rapidly. Over the years, with the evolution of the intelligent computing methods, many rainfall prediction methods have been proposed, Artificial Neural Network being one of the most prominent. Since the last decade, many researchers have proposed different artificial neural network models in order to create accurate rainfall prediction models. In this paper, different artificial neural networks have been created for the rainfall prediction of Pondicherry, a coastal region in India. These ANN models were created using three different training algorithms namely, feed-forward back propagation algorithm, layer recurrent algorithm and feedforward distributed time delay algorithm. The number of neurons for all the models was kept at 20. The mean squared error was measured for each model and the best accuracy was obtained by feed-forward distributed time delay algorithm with MSE value as low as .0083.