Covid-19 is one of the biggest issues of the world. It is an untreatable and communicable disease. Therefore lots of countries are in a lockdown process. Our government has also announced lockdown since mid of March 2020. During this pandemic people are feeling psychological presser like anxiety, depression and stress because they are not able to come out their homes and are not in a position to fulfill their needs. In this situation central and state government has launched various types of counseling programs to reduce their psychological presser. Counseling is one of the best strategies in which counselors provide support to the people to keep them healthy. Now, this is a part of regular follow up by psychologists, during this pandemic of covid-19. The main intention of this study is to know the impact of counseling on the psychological health of adults. There were two groups in the study-one group was counseling taker (including quarantined people) and another group was without counseling taker. Every group had 50 adults and they were selected by purposive sampling method through online and from quarantine centre. All the samples were administered Anxiety, Depression and Stress Scale to measure their psychological health. The present study reveals that counseling taker adults had less anxiety, depression

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Deep learning is a powerful technique which is inspired by the structure as well as processing power of the human brain. This technique uses deep neural network to perform complex tasks such as time series prediction, image classification, and cancer detection. In this research work, we used Covid-19 time series datasets and with the help of deep learning we built the model for prediction of Covid-19 cases. For the model building, we used two deep learning neural networks, Recurrent Neural Networks (RNN) and Long Short Term Memory Networks (LSTMs). We built a prediction model using RNN in the first instance and subsequently the second model was built using LSTMs. Out of these two neural networks, we got promising results from the model based on LSTMs with an overall accuracy of 98%. As the cases of Covid-19 are increasing day-by-day at a very high rate, we proposed these models using neural networks to help in predicting the future trends of Covid-19 confirmed, deaths and recovered cases.

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Because of simple synthetic strategies, randomly functionalized amphiphilic polymers have gained much attention. Recent studies have demonstrated that such polymers can be reorganized into different nanostructures, such as spheres, cylinders, vesicles, etc., similar to amphiphilic block copolymers. Our study investigated the self-assembly of randomly functionalized hyperbranched polymers (HBP) and their linear analogues (LP) in solution and at the liquid crystal−water (LC−water) interfaces. Regardless of their architecture, the designed amphiphiles self-assembled into spherical nanoaggregates in solution and mediated the ordering transitions of LC molecules at the LC− water interface. However, the amount of amphiphiles required for LP was 10 times lower than that required for HBP amphiphiles to mediate the same ordering transition of LC molecules. Further, of the two compositionally similar amphiphiles (linear and branched), only the linear architecture responds to biorecognition events. The architectural effect can be attributed to both of these differences mentioned above.