Background: The COVID-19 pandemic has led to a complete shut-down of the entire world and almost all the countries are presently in a “lockdown” mode. While the lockdown strategy is an essential step to curb the exponential rise of COVID-19 cases, the impact of the same on mental health is not well known. Aim: This study aimed to evaluate the psychological impact of lockdown due to COVID-19 pandemic on the general public with an objective to assess the prevalence of depression, anxiety, perceived stress, well-being, and other psychological issues. Materials and Methods: It was an online survey conducted under the aegis of the Indian Psychiatry Society. Using the Survey Monkey platform, a survey link was circulated using the Whatsapp. The survey questionnaire included perceived stress scale, Patient Health Questionnaire-9, Generalized Anxiety Disorder-7, Warwick-Edinburgh Mental Well-being Scale to assess perceived stress, anxiety, depression, and mental well-being, respectively. The survey link was circulated starting from April 6, 2020 and was closed on April 24, 2020. Results: During the survey, a total of 1871 responses were collected, of which 1685 (90.05%) responses were analyzed. About two-fifth (38.2%) had anxiety and 10.5% of the participants had depression. Overall, 40.5% of the participants had either anxiety or depression. Moderate level of stress was reported by about three-fourth (74.1%) of the participants and 71.7% reported poor well-being. Conclusions: The present survey suggests that more than two-fifths of the people are experiencing common mental disorders, due to lockdown and the prevailing COVID-19 pandemic. This finding suggests that there is a need for expanding mental health services to everyone in the society during this pandemic situation.

Text Categorization (TC), also known as Text Classification, is the task of automatically classifying a set of text documents into different categories from a predefined set. If a document belongs to exactly one of the categories, it is a single-label classification task; otherwise, it is a multi-label classification task. TC uses several tools from Information Retrieval (IR) and Machine Learning (ML) and has received much attention in the last years from both researchers in the academia and industry developers. In this paper, we first categorize the documents using KNN based machine learning approach and then return the most relevant documents.

Background: Coronavirus disease 2019 (COVID-19) has emerged as a global health threat. The South-Asian (SA) countries have witnessed both the initial brunt of the outbreak as well as the ongoing rise of cases. Their unique challenges in relation to mental health during the pandemic are worth exploring. Materials and Methods: A systematic review was conducted for all the original studies on the impact of COVID-19 and lockdown on psychological health/well-being in the SA countries of the World Psychiatric Association Zone 16. PubMed, Google Scholar, PSYCHINFO, EMBASE, and SCOPUS were searched till June 2020. Studies conducted in the age group of 18–60 years with a minimum sample size of 10, and statistically significant results were included. Results: Thirteen studies were included in the review. They showed increase prevalence in nonpsychotic depression, preanxiety, somatic concerns, alcohol-related disorders, and insomnia in the general population. Psychological symptoms correlated more with physical complaints of fatigue and pain in older adults and were directly related to social media use, misinformation, xenophobia, and social distancing. Frontline workers reported guilt, stigma, anxiety, and poor sleep quality, which were related to the lack of availability of adequate personal protective equipment, increased workload, and discrimination. One study validated the Coronavirus anxiety scale in the Indian population while another explored gaming as a double-edged sword during the lockdown in adolescents. Another study from Bangladesh explored psychosexual health during lockdown. Most studies were cross-sectional online surveys, used screening tools and had limited accessibility. Conclusion: The ongoing COVID-19 crisis and its impact serve as an important period for adequate mental healthcare, promotion, research, and holistic biopsychosocial management of psychiatric disorders, especially in vulnerable groups. Mental healthcare and research strategies during the pandemic and preparedness for postpandemic aftermath are advocated subsequently.

With the rapid increase in the use of the Internet, sentiment analysis has become one of the most popular fields of natural language processing (NLP). Using sentiment analysis, the implied emotion in the text can be mined effectively for different occasions. People are using social media to receive and communicate different types of information on a massive scale during COVID-19 outburst. Mining such content to evaluate people's sentiments can play a critical role in making decisions to keep the situation under control. The objective of this study is to mine the sentiments of Indian citizens regarding the nationwide lockdown enforced by the Indian government to reduce the rate of spreading of Coronavirus. In this work, the sentiment analysis of tweets posted by Indian citizens has been performed using NLP and machine learning classifiers. From April 5, 2020 to April 17, 2020, a total of 12 741 tweets having the keywords "Indialockdown" are extracted. Data have been extracted from Twitter using Tweepy API, annotated using TextBlob and VADER lexicons, and preprocessed using the natural language tool kit provided by the Python. Eight different classifiers have been used to classify the data. The experiment achieved the highest accuracy of 84.4% with LinearSVC classifier and unigrams. This study concludes that the majority of Indian citizens are supporting the decision of the lockdown implemented by the Indian government during corona outburst.

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In the past several decades, significant attention has been devoted to the quality assessment of safety-critical (SC) and control systems from many perspectives such as its reliability, safety, and performance. Researchers are continuing to put their efforts to ensure these dependability attributes. This study summarises the state of the art in the field of the reliability of such systems. A detailed literature survey is conducted to investigate the various techniques/models to ensure the reliability of the computer-based systems. The limitations of these models are also analysed with respect to their applicability in SC systems, for which a case study of nuclear power plant system has been taken. The direction for future research is suggested, based on the case study, to extend the further scope of research. 19 April 06 medium delay of 120 flights false alarm due to malfunction of software

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