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Mining is an important enterprise that will supply essential minerals for the country's infrastructural development. Mining is a key industry and a development engine in the Indian economy. The epidemic has had both immediate and long-term effects on the labour and industries in India. Short-term effects include the cessation of mineral production, salary loss due to absenteeism and pandemic limitations, unemployment, and the selling of minerals on the market. The reputation and economic health of industries, fresh mineral deposit exploration, and new mining complex development have all been impacted in long-term. Aside from the mining industry-specific impact of COVID-19, the social fabric of the mining workers has been impacted since they are living in an environment of fear and uncertainty. This paper attempts to analyse the effect of COVID-19 on extractive industries in India. It also focuses the steps which taken by the Indian government. The paper concluded with the negative effect of COVID-19 which disrupts the mining sector and the contribution declined during COVID-19. However, for instance from mineral rich-Odisha, the mining sector takes up by its growth in post COVID-19 period with remarkable increment from negative growth to high positive rate. [ABSTRACT FROM AUTHOR]

The corona pandemic altered many traditional and historical norms of society and law. COVID-19 created a humanitarian crisis in some parts of globe, while pandemic privacy and civil liberties were under threat all over world. To combat the deadly virus, individual liberty and equality were compromised. This paper focuses on how India's health problem has compromised people's right to privacy. It will highlight how strict executive policies led to the creation of a massive surveillance system in the name of combating the COVID-19 pandemic, as well as how the absence of any policy or legal framework led to the exclusion of individuals and their families who were suspected of having the virus or caring for those who were infected with the deadly virus. The paper uses case studies and data collected from primary as well as secondary sources. The authors will also point out how the absence of privacy regulation puts millions of citizens' private information at risk of being compromised or exploited against their will. [ABSTRACT FROM AUTHOR]

The objective of this paper is to provide an insight on effect of stringency in Covid-19 spread in India especially in Chennai, a city were more lockdown, and restrictions was imposed to control the infection. Even though the restriction was imposed in the country by the end of March 2020, the growth reduction was seen in the mid of June as the awareness was increased. The average Covid-19 case growth was got reduce from 3.43 to 2.62% by July mid. To analysis the impact of stringency, a detailed analysis was done on Chennai city which was imposed with more repeated lockdowns to flatten the curve. We tried to fit a regression line with three difference scenario of data. The results show a promising R-squared and p value, with a right skewed distribution normal probability plot. The impact of lockdown in people's lives in different sectors were also discussed in this paper. [ABSTRACT FROM AUTHOR]

The paper investigates the spread pattern and dynamics of Covid-19 propagation based on SIR model. Using the model dynamics, an analytical estimation has been obtained for virus span, its longevity, growing pattern, etc. Experimental simulations are carried out on the data of four regions of India over a period of two months of country-wide lockdown. The analysis illustrates the effect of lockdown on the contact rate and its implication. Simulation results illustrate that there is a cut-down in effective contact rate by a considerable factor ranging from 2 to 4 for the selected regions. Further, the estimates for the vaccines to be developed, maximum range and span of the disease can be also estimated. Results portray that the SIR model is a significant tool to cast the dynamics and predictions of Covid-19 outbreak in comparison to other epidemic models. The study demonstrates the progression of real time data in accordance with the SIR model with high accuracy. [ABSTRACT FROM AUTHOR]

The world is currently facing a pandemic called COVID-19 which has drastically changed our human lifestyle, affecting it badly. The lifestyle and the thought processes of every individual have changed with the current situation. This situation was unpredictable, and it contains a lot of uncertainties. In this paper, the authors have attempted to predict and analyze the disease along with its related issues to determine the maximum number of infected people, the speed of spread, and most importantly, its evaluation using a model-based parameter estimation method. In this research the Susceptible-Infectious-Recovered model with different conditions has been used for the analysis of COVID-19. The effects of lockdown, the light switch method, and parameter variations like contact ratio and reproduction number are also analyzed. The authors have attempted to study and predict the lockdown effect, particularly in India in terms of infected and recovered numbers, which show substantial improvement. A disease-free endemic stability analysis using Lyapunov and LaSalle's method is presented, and novel methods such as the convalescent plasma method and the Who Acquires Infection From Whom method are also discussed, as they are considered to be useful in flattening the curve of COVID-19. [ABSTRACT FROM AUTHOR]

Although the narrative of modernity has been rejected by numerous scientists, it remains a powerful paradigm. Several Western countries have, during the COVID-19 pandemic, witnessed renewed interest in some archaic practices and beliefs. Based mostly on media analysis, this paper provides an insight into religious responses to the COVID-19 pandemic in Slovakia and India, two completely different cultural areas. Simultaneously, it challenges the self-identification of the West as the epicentre of rational thought, in contrast to the so-called non-Western world. The self-image of superiority adopted by the modern West in terms of religious relevance has proved to be distorted, as the tendency to turn to spiritual practices in times of crisis is not exclusively a feature of non-Western societies. [ABSTRACT FROM AUTHOR]

The World Health Organization announced on March 11, 2020 that COVID-19 could become a pandemic. COVID-19 is a contagious disease caused by the coronavirus that causes severe acute respiratory syndrome (SARS-CoV-2). Viruses usually enter the body through the mouth or nose. The virus then enters the alveoli, which are small air sacs inside the lungs. Cough, fatigue, fever, shortness of breath or breathing difficulties, and loss of smell and taste are all symptoms of COVID-19. Anosmia, also known as smell blindness, is a condition in which the ability to detect one or more smells is lost. Olfaction uses chemoreceptors to create signals that are processed in the brain and form the sense of smell in anosmia. Anosmia is recognised as a COVID-19 symptom in many countries, and some have developed "smell tests" as potential screening tools. The first level of screening, which is currently used in India, is primarily based on temperature and can result in false positives and negatives (fever as a symptom has not yet been developed although infection). One of the methods for detecting COVID-19 is an intermediate level of screening based on assessing an olfactory function, depending on the usage. This paper provides an overview of COVID-19 and its effects on the human body, as well as an overview of anosmia and how it contributes to one of the symptoms of COVID-19. [ABSTRACT FROM AUTHOR]

Corona Virus Disease 2019 (COVID-19) is a continuing extensive incident globally affecting several million people's health and sometimes leading to death. The outbreak prediction and making cautious steps is the only way to prevent the spread of COVID-19. This paper presents an Adaptive Neuro-fuzzy Inference System (ANFIS)-based machine learning technique to predict the possible outbreak in India. The proposed ANFIS-based prediction system tracks the growth of epidemic based on the previous data sets fetched from cloud computing. The proposed ANFIS technique predicts the epidemic peak and COVID-19 infected cases through the cloud data sets. The ANFIS is chosen for this study as it has both numerical and linguistic knowledge, and also has ability to classify data and identify patterns. The proposed technique not only predicts the outbreak but also tracks the disease and suggests a measurable policy to manage the COVID-19 epidemic. The obtained prediction shows that the proposed technique very effectively tracks the growth of the COVID-19 epidemic. The result shows the growth of infection rate decreases at end of 2020 and also has delay epidemic peak by 40–60 days. The prediction result using the proposed ANFIS technique shows a low Mean Square Error (MSE) of 1.184 × 10–3 with an accuracy of 86%. The study provides important information for public health providers and the government to control the COVID-19 epidemic. [ABSTRACT FROM AUTHOR]

The rapid spread of the novel corona virus disease (COVID-19) has disrupted the traditional clinical services all over the world. Hospitals and healthcare centers have taken extreme care to minimize the risk of exposure to the virus by restricting the visitors and relatives of the patients. The dramatic changes happened in the healthcare norms have made it hard for the deaf patients to communicate and receive appropriate care. This paper reports a work on automatic sign language recognition that can mitigate the communication barrier between the deaf patients and the healthcare workers in India. Since hand gestures are the most expressive components of a sign language vocabulary, a novel dataset of dynamic hand gestures for the Indian sign language (ISL) words commonly used for emergency communication by deaf COVID-19 positive patients is proposed. A hybrid model of deep convolutional long short-term memory network has been utilized for the recognition of the proposed hand gestures and achieved an average accuracy of 83.36%. The model performance has been further validated on an alternative ISL dataset as well as a benchmarking hand gesture dataset and obtained average accuracies of 97 % and 99.34 ± 0.66 % , respectively. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has exacerbated the already existing gender inequalities with substantial implications on women. With the closure of offices and educational institutions, and the emerging norm of work from home and online education, along with the lack of services of domestic worker, the need to perform unpaid chores in the household has increased. Simultaneously, the requirements of social distancing and sanitization have created new unpaid chores. Owing to the sexual division of labour, and gendered roles and social norms of performing domestic and care work, the burden of unpaid work falls disproportionately on women. In this context, the objective of the paper is to study the impact of COVID-19 on time spent on unpaid work and the underlying gender differences in the urban centres in India. Specifically, the paper will do a comparative analysis of the gender differences in time spent on unpaid work before and during the lockdown, and analyse the reasons for the same. [ABSTRACT FROM AUTHOR]

Even after scavenging the existence of mankind for the past year, the wrath of CoVID-19 is yet to die down. Countries like India are still getting haunted by the devastating conundrum, with coronavirus ripping through its citizens in the concurrent second wave. The surge of cases has prompted rapid intervention, with medical authorities pushing it to the limit to curve a roadblock to its aggressive growth. But, even after effortless work, human intervention remains slow and insufficient. Furthermore, relevant testing methodologies have shown weakness while detecting threats, with the recent growth of post-Covid complexities, thereby leaving a painful mark. This as such created a major requirement for technological advancements, which can cater to the mass. The growth of computational prowess in the past decade made the field of Deep Learning a major contributor in curving out algorithms to solve this. Adding to the excellent foundation of Deep Learning, this paper, proposes a novel CoWarriorNet model for rapid detection of CoVID-19, via chest X-ray images, which adds in an extra layer of precision and confirmation in the detection of cases in both pre-Covid and post-Covid conditions. The proposed classification model curves out an excellent accuracy of 97.8%, with the major eye-candy being the sensitivity rate of 0.99 when detecting CoVID-19 cases. This model introduces a new concept of Alpha Trimmed Average Pooling, which along with the novel architecture adds a subtle touch to its high efficiency, thereby giving a much-needed solution to the medical experts. The two-mouthed architecture provides the added benefit of a confidence score, deducing human aid in case of discrepancy. [ABSTRACT FROM AUTHOR]

Coronavirus disease so called as COVID-19 is an infectious disease and its spread takes place due to human interaction by their pathogen materials during coughing and sneezing. COVID-19 is basically a respiratory disease as evidence proved that a large number of infected people died due to short breathing. Most widely and uncontrollably spreading unknown viral genome infecting people worldwide was announced to be 2019–2020 nCoV by WHO on January 30, 2020. Based on the seriousness of its spread and unavailability of vaccination or any form of treatment, it was an immediate health-emergency of concern of international-level. The paper analyses effects of this virus in countries, such as India and United States on day-to-day basis because of their greater variability. In this study, various performance measures, such as root mean square error (RMSE), mean absolute error (MAE), coefficient of determination (R 2) , mean absolute standard error (MASE) and mean absolute percentage error (MAPE) which characterize models' performances. R 2 value has been achieved to be closest to 1, i.e., 0.999 from Wavelet Neuronal Network Fuzzified Inferences' Layered Multivariate Adaptive Regression Spline (WNNFIL–MARS) for both the countries' data. It is important to capture the essence of this pandemic affecting millions of the population daily ever since its spread began from January, 2020. [ABSTRACT FROM AUTHOR]

India is one of the worst hit regions by the second wave of COVID-19 pandemic and 'Black fungus' epidemic. This paper revisits the Bombay Plague epidemic of India and presents six fractional-order models (FOMs) of the epidemic based on observational data. The models reveal chaotic dispersion and interactive coupling between multiple species of rodents. Suitable controllers based on fuzzy logic concept are designed to stabilise chaos to an infection-free equilibrium as well as to synchronise a chaotic trajectory with a regular non-chaotic one so that the unpredictability dies out. An FOM of COVID-19 is also proposed that displays chaotic propagation similar to the plague models. The index of memory and heredity that characterise FOMs are found to be crucial parameters in understanding the progression of the epidemics, capture the behaviour of transmission more accurately and reveal enriched complex dynamics of periodic to chaotic evolution, which otherwise remain unobserved in the integral models. The theoretical analyses successfully validated by numerical simulations signify that the results of the past Plague epidemic can be a pathway to identify infected regions with the closest scenarios for the present second wave of Covid-19, forecast the course of the outbreak, and adopt necessary control measures to eliminate chaotic transmission of the pandemic. [ABSTRACT FROM AUTHOR]

India is a hotspot of the COVID-19 crisis. During the first wave, several lockdowns (L) and gradual unlock (UL) phases were implemented by the government of India (GOI) to curb the virus spread. These phases witnessed many challenges and various day-to-day developments such as virus spread and resource management. Twitter, a social media platform, was extensively used by citizens to react to these events and related topics that varied temporally and geographically. Analyzing these variations can be a potent tool for informed decision-making. This paper attempts to capture these spatiotemporal variations of citizen reactions by predicting and analyzing the sentiments of geotagged tweets during L and UL phases. Various sentiment analysis based studies on the related subject have been done; however, its integration with location intelligence for decision making remains a research gap. The sentiments were predicted through a proposed hybrid Deep Learning (DL) model which leverages the strengths of BiLSTM and CNN model classes. The model was trained on a freely available Sentiment140 dataset and was tested over manually annotated COVID-19 related tweets from India. The model classified the tweets with high accuracy of around 90%, and analysis of geotagged tweets during L and UL phases reveal significant geographical variations. The findings as a decision support system can aid in analyzing citizen reactions toward the resources and events during an ongoing pandemic. The system can have various applications such as resource planning, crowd management, policy formulation, vaccination, prompt response, etc. [ABSTRACT FROM AUTHOR]

The exponential spread of Covid-19 is not only a serious concern for public health but has also severely affected the global economy. India is not an exception. The banking sector must plan innovatively in a wide range of scenarios focusing upon Covid-19 specific requirements. It becomes essential to examine the impact of Covid-19 on the performance of the Indian banking sector and take focused initiatives at both the tactical and the strategic levels. This paper offers the Covid-19 Impact on Banking Ontology (Covid19-IBO) that provides semantic information about the impact of Covid-19 on the banking sector of India. The developed ontology has been verified and validated and has been made available on the Linked Open Data cloud. It can be utilized to annotate the related data to provide meaningful insights. The Covid-19 ontologies already available have some overlapping information that causes redundancy. Unified integration of these ontologies is required to operate upon them unambiguously. It becomes reasonable to develop a matching approach to link all these ontologies semantically. We, therefore, also provide a schema matching approach with reasonable results to map the Covid-19 ontologies. [ABSTRACT FROM AUTHOR]

Novel Coronavirus (COVID-19) has become one of the deadliest pandemics that has affected almost all the nations in the world. Lockdown and systematic re-opening of shopping malls, offices, etc. is still one of the major weapons against this virus. However, the government and medical agencies take long time to reopen the places due to risks involved in this deadly virus. The delay to reopen places has resulted in sharp decline in the growth of economy. In this paper a current context aware framework is proposed which uses multiple inputs for a specific region to decide whether to open it or not. The proposed framework used series of deep neural network models to generate recommendations specific to a particular region. Most of the inputs are real-time and readily available with the government. The main aim is to develop framework which can be used in any kind of pandemic even in small region to easily contain it. However, it has been tested using opensource data available for COVID-19. Data was crawled from web for 22 districts of Haryana state of India. Experimental result proved the efficiency of proposed framework. [ABSTRACT FROM AUTHOR]

The Novel Coronavirus which emerged in India on January/30/2020 has become a catastrophe to the country on the basis of health and economy. Due to rapid variations in the transmission of COVID-19, an accurate prediction to determine the long term effects is infeasible. This paper has introduced a nonlinear mathematical model to interpret the transmission dynamics of COVID-19 infection along with providing vaccination in the precedence. To minimize the level of infection and treatment burden, the optimal control strategies are carried out by using the Pontryagin's Maximum Principle. The data validation has been done by correlating the estimated number of infectives with the real data of India for the month of March/2021. Corresponding to the model, the basic reproduction number R 0 is introduced to understand the transmission dynamics of COVID-19. To justify the significance of parameters we determined the sensitivity analysis of R 0 using the parameters value. In the numerical simulations, we concluded that reducing R 0 below unity is not sufficient enough to eradicate the COVID-19 disease and thus, it is required to increase the vaccination rate and its efficacy by motivating individuals to take precautionary measures. [ABSTRACT FROM AUTHOR]

Mining is an essential industry that will provide key materials needed for India infrastructure development. Mining is one of the core sectors and growth driver of Indian economy. Minerals and ores provide basic raw materials to many important industries like power generation (thermal), iron and steel, cement, petroleum and natural gas, petrochemicals, fertilizers, precious and semi-precious metals/stones for jewellery, electrical and electronics equipment, glass, ceramics, etc. The mining sector in India was poised for robust growth in financial year 2020–2021, on the back of rising demand from end-use sectors and fresh investments announced by the mining companies. However, the spread of COVID-19, right at the beginning of the financial year, has led to disruptions across industries. There is a significant scope for new mining capacities in iron ore, bauxite, coal and other minerals and considerable opportunities for future discoveries of sub-surface deposits. Infrastructure projects continue to provide lucrative business opportunities for steel, zinc and aluminium producers. Iron and steel make up a core component for the real estate sector. Demand for these metals is set to continue given strong growth expectations for the residential and commercial building industry. India's mining sector expected to show positive growth in 2021 compared to 2020. As different arms of the government work with stakeholders to bring the economy on the growth path, it is important for the policymakers of mining to continue making such changes to the policies, which can ramp up the contribution of the sector to GDP. In the present paper, an overview of impact of lockdown on the mining industry in India is discussed with special reference to production scenario of minerals, employment, export-import scenario of ores and minerals and other developments during the period. [ABSTRACT FROM AUTHOR]

The paper has a dual focus. One, is to use India as a case study to reflect upon the political, economic and socio-cultural structures of the country and how they shape the particular vulnerabilities in the society which have been aggravated in the COVID19 crisis-scape. Stories about Indian democracy, Casteism and Untouchability, Hindutva and Islamophobia emerging from the pandemic scenario have been extended to show global parallels. Stories of cultural resourcefulness and examples of people's power in the Indian context have also been represented to provide a formative assessment of how India is faring in the pandemic crisis. Two, it gathers emergent questions about human and social life which are being re-emphasized and created by the pandemic for Psychology to focus on. It makes use of Indian psychological concepts to re-envision self-other relationships in Post Coronial world. It makes a plea for strengthening the critical-cultural approach in 'Post-Coronial' Psychology. [ABSTRACT FROM AUTHOR]

Background: The declaration of COVID pandemic by the WHO can certainly be seen as a watershed era the world has witnessed in modern times. All non-essential industries and services have taken a back seat including aesthetic medicine. Over the last decade, India has witnessed a steady growth in medical tourism owing to global standards of care and services at a relatively modest cost. The following study was conducted to ascertain the sea change that this pandemic has brought into aesthetic surgeons' practice, patient management, planning and consultation. This paper throws light on the journey of Indian aesthetic surgery from its infancy to its current presence in the global market as a context of the study. We have also discussed the impact of social media on aesthetic surgeons' practice, lifestyle and its role as an emerging new method of medical education. Methods: A questionnaire consisting of 62 questions divided in 3 sections was rolled out to 150 Indian aesthetic surgeons who have been practising either independently in their clinics or are associated with hospitals. A: Pre-COVID practice management and lifestyle; B: life during the lockdown; C: anticipated changes in post-COVID era. Results: In the pre-COVID era, an average aesthetic surgeon was finely balancing his profession, personal lifestyle, learning, and recreation. The lockdown clamped their practices which lead into a financial drought; despite which, they were able to maintain their productivity by engaging in webinars, reading, and research. The post-COVID times demand an implementation of safety protocols along with changes in set-up, regulating patient traffic, engaging in distant learning through virtual conferences, and maintaining a healthy lifestyle acquired during the lockdown. Conclusions: India was rightly witnessing a surge in popularity of aesthetic surgery and medical tourism over the last decade. The corona pandemic has definitely hit this escalating growth curve hard, and it will take some time for the demand to recover. Our study revealed the following conclusions: The effect of COVID 19 demands a major change in aesthetic surgeons' professional practice like limiting consultations, changing hospital floor plan, following COVID testing, and having new safety protocols. Social media is rightly poised to be a major tool for education and marketing as also for recreation and leisure. The role of teleconsultation needs to be reprised and legalised. Webinars and virtual conferences will find more takers in future. Level of evidence: not ratable. [ABSTRACT FROM AUTHOR]