ABSTRACT
The COVID-19 outbreak resulted in multiple waves of infections that have been associated with different SARS-CoV-2 variants. Studies have reported differential impact of the variants on respiratory health of patients. We explore whether acoustic signals, collected from COVID-19 subjects, show computationally distinguishable acoustic patterns suggesting a possibility to predict the underlying virus variant. We analyze the Coswara dataset which is collected from three subject pools, namely, i) healthy, ii) COVID-19 subjects recorded during the delta variant dominant period, and iii) data from COVID-19 subjects recorded during the omicron surge. Our findings suggest that multiple sound categories, such as cough, breathing, and speech, indicate significant acoustic feature differences when comparing COVID-19 subjects with omicron and delta variants. The classification areas-under-the-curve are significantly above chance for differentiating subjects infected by omicron from those infected by delta. Using a score fusion from multiple sound categories, we obtained an area-under-the-curve of 89% and 52.4% sensitivity at 95% specificity. Additionally, a hierarchical three class approach was used to classify the acoustic data into healthy and COVID-19 positive, and further COVID-19 subjects into delta and omicron variants providing high level of 3-class classification accuracy. These results suggest new ways for designing sound based COVID-19 diagnosis approaches. Copyright © 2022 ISCA.
ABSTRACT
In this paper, we describe an approach for representation learning of audio signals for the task of COVID-19 detection. The raw audio samples are processed with a bank of 1-D convolutional filters that are parameterized as cosine modulated Gaussian functions. The choice of these kernels allows the interpretation of the filterbanks as smooth band-pass filters. The filtered outputs are pooled, log-compressed and used in a self-attention based relevance weighting mechanism. The relevance weighting emphasizes the key regions of the time-frequency decomposition that are important for the downstream task. The subsequent layers of the model consist of a recurrent architecture and the models are trained for a COVID-19 detection task. In our experiments on the Coswara data set, we show that the proposed model achieves significant performance improvements over the baseline system as well as other representation learning approaches. Further, the approach proposed is shown to be uniformly applicable for speech and breathing signals and for transfer learning from a larger data set. Copyright © 2022 ISCA.
ABSTRACT
The COVID-19 pandemic has accelerated research on design of alternative, quick and effective COVID-19 diagnosis approaches. In this paper, we describe the Coswara tool, a website application designed to enable COVID-19 detection by analysing respiratory sound samples and health symptoms. A user using this service can log into a website using any device connected to the internet, provide there current health symptom information and record few sound sampled corresponding to breathing, cough, and speech. Within a minute of analysis of this information on a cloud server the website tool will output a COVID-19 probability score to the user. As the COVID-19 pandemic continues to demand massive and scalable population level testing, we hypothesize that the proposed tool provides a potential solution towards this. Copyright © 2022 ISCA.
ABSTRACT
Purpose: The global pandemic and the resulting rapid and large-scale digitization changed the way firms recognized and understood knowledge curation and management. The changing nature of work and work systems necessitated changes in knowledge management (KM), some of which are likely to have a long-term impact. Using the lens of technology in practice, the purpose of this study is to examine the impact of technology agency on KM structures and practices that evolved across five knowledge-intensive global organizations. This study then argues that sustainable knowledge management (SKM) systems evolve in specific contexts. Design/methodology/approach: This study adopts a qualitative case study design to examine five multinational knowledge-intensive global organizations’ KM systems and practices across diverse industry sectors. Findings: Based on the findings, the authors develop SKM systems and practices model relevant to a post-pandemic organizational context. The authors argue that KM digitization and adoption support socialization in knowledge sharing. Further formalization through organizational enabling systems aids the externalization of knowledge sharing. Deliberate practices promoted with leadership support are likely to sustain in the post-COVID era. Further, organizations that evolved ad-hoc or idiosyncratic approaches to managing hybrid working are more likely to revert to legacy KM systems. The authors eventually theorize about the socialization of human-to-human and technology-mediated human interactions and develop the three emerging SKM structures. Originality/value: This study contributed to practitioners and researchers by developing the various tenets of SKM. © 2022, Emerald Publishing Limited.
ABSTRACT
The demonic progression of the SARS-CoV-2 (severe acute respiratory syndrome coronavirus) worldwide points out the need to develop innovative, rapid, and sensitive detection of microbial pathogens. Conventional molecular diagnostic techniques often require sophisticated, expensive instrumentation in addition to the high cost and shorter shelf life of some reagents. In contrast, the use of fluorescent quantum dots, carbon nanotube, etc., for pathogen detection can be tailored by changing surface charge ratio and particle size, which may provide global accessibility. The operating principle behind pathogen identification is the surface marker recognition of bacterial or viral nucleic acid sequences. Therefore this chapter aims to focus on the comprehensive utilization of nanotechnology in pathogen identification. The emerging technology of nano-based point-of-care detection and its association with the neural network is believed to mark a blueprint for diagnosing infectious diseases and improving human existence. © 2022 Elsevier Inc. All rights reserved.
ABSTRACT
The evolution of online food delivery system started in India in the late 2000’s and since then many Food Aggregators have come up with a variety of prospects for the customers. This process of Business to Customer services had found itself to be very popular especially in the last few years and after the COVID 19 attack the business had flourished to a large extent. People do not prefer to come out of their abodes and try to procure the eatables by maintaining proper social distancing. There have been a number of local Food Aggregators that have emerged in the Cachar District only recently and post 2020 especially in the lockdown phase they have accelerated their operations in the Valley by joining hands with a number of food outlets. These local entrepreneurial efforts are still in the growth phase and are trying to meet the customer demands to enhance their satisfaction level. Speaking of enhancing the satisfaction of the customers, there are many factors that work before meeting their overall satisfaction and these factors if are considered carefully would not only increase the customer loyalty towards the respective. Purposive Sampling was used in this study to get the responses from the online food buyers. It used Artificial Neural Networks to understand the pattern of the buying behavior of customers in this area and tried to create a model that would enhance the understanding of the Food Aggregators in regards to the buying frequency of the customers and take steps accordingly. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
Despite the introduction of vaccines and drugs for SARS-CoV-2, the COVID-19 pandemic continues to spread throughout the world. In severe COVID-19 patients, elevated levels of proinflammatory cytokines have been detected in the blood, lung cells, and bronchoalveolar lavage, which is referred to as a cytokine storm, a consequence of overactivation of the NLR family pyrin domain-containing protein 3 (NLRP3) inflammasome and resultant excessive cytokine production. The hyperinflammatory response and cytokine storm cause multiorgan impairment including the central nervous system, in addition to a detriment to the respiratory system. Hyperactive NLRP3 inflammasome, due to dysregulated immune response, is the primary cause of COVID-19 severity. The severity could be enhanced due to viral evolution leading to the emergence of mutated variants of concern, such as delta and omicron. In this review, we elaborate on the inflammatory responses associated with the NLRP3 inflammasome activation in COVID-19 pathogenesis, the mechanisms for the NLRP3 inflammasome activation and pathway involved, cytokine storm, and neurological complications as long-term consequences of SARS-CoV-2 infection. Also discussed is the therapeutic potential of NLRP3 inflammasome inhibitors for the treatment of COVID-19.
ABSTRACT
The pandemic COVID-19 is an infectious respiratory illness caused by SARS CoV-2 (severe acute respiratory syndrome coronavirus-2) and it spreads human-to-human. Due to the COVID-19 outbreak, the world is facing an unprecedented loss of lives around the globe and highlighted an effective treatment to deal with the virus. Natural products have historically been utilized for respiratory disease and display promising toxicity. Natural products have been reported for several antiviral activities of viruses, like influenza, HIV and some coronaviruses SARS-CoV and MERS-CoV. Therefore, natural products could be a vital resource for developing efficient and safe antiviral drugs against COVID-19. This review summarized the inhibition of isolated compounds from medicinal plants against different coronaviruses which could lead to the development of effective antiviral drugs to counter COVID-19. © 2022, Rasayan Journal of Chemistry, c/o Dr. Pratima Sharma. All rights reserved.
ABSTRACT
The Second Diagnosis of COVID-19 using Acoustics (DiCOVA) Challenge aimed at accelerating the research in acoustics based detection of COVID-19, a topic at the intersection of acoustics, signal processing, machine learning, and healthcare. This paper presents the details of the challenge, which was an open call for researchers to analyze a dataset of audio recordings consisting of breathing, cough and speech signals. This data was collected from individuals with and without COVID-19 infection, and the task in the challenge was a two-class classification. The development set audio recordings were collected from 965 (172 COVID-19 positive) individuals, while the evaluation set contained data from 471 individuals (71 COVID-19 positive). The challenge featured four tracks, one associated with each sound category of cough, speech and breathing, and a fourth fusion track. A baseline system was also released to benchmark the participants. In this paper, we present an overview of the challenge, the rationale for the data collection and the baseline system. Further, a performance analysis for the systems submitted by the 21 participating teams in the leaderboard is also presented. © 2022 IEEE
ABSTRACT
Health care has been a major concern for everyone right from the inception of mankind. There is no doubt that medical science has done remarkable headway in this arena too. Here, the detection of disease a person is suffering from is a key aspect and hence, in this project we proposed a model which primarily focuses on having easy diagnosis and prediction of the disease. Moreover, the primary objective of this project is to provide remote diagnosis to the patients. In this suggested system the user can provide the input either by speech or entering directly into the UI. The proposed model once detecting the disease also displays the description of the same and a more info link for further elucidation. Along with it the user is also provided with important health tips, a balanced diet plan and required exercises which will help the user to make the required changes in their diet and daily routine which would lead to a potential healthy lifestyle. Apart from this, as per the current situation of the ongoing pandemic we also added few other features other than the above mentioned like a COVID-19 tracker which helps the user to stay updated with the state wise count which gives us the total confirmed,active, recovered and deceased cases according to the Ministry of Health and Family Welfare department and it is purely dynamic in nature. Also, we are providing the potential causes of the disease and the preventive measures to be taken by the user as per the government guidelines. By this, we are trying to achieve better health care in a technological aspect. Using the latest technologies will make it much efficient to know about various symptoms and predict the cause at an early stage which will help in taking necessary steps to minimize the damage. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
ABSTRACT
Despite the widespread disruptions of lives and livelihoods due to the COVID-19 pandemic, it could also be seen as a gamechanger. The post-pandemic recovery should address fundamental questions concerning our food systems. Is it possible to reset existing ecologically unsustainable production systems towards healthier and more connected systems of conscious consumers and ecologically oriented farmers? Based on three illustrative cases from different parts of India, we show how managing transitions towards sustainability require institutional innovations and new intermediaries that build agency, change relations, and transform structures in food systems. Lessons from three diverse geographies and commodities in India are presented: Urban farming initiatives in Mumbai, conscious consumer initiatives in semi-urban Gujarat for pesticide-free mangoes, and resource-poor arid regions of Andhra Pradesh. Through these examples, we show that, beyond the technological solutions, institutional innovations such as urban community-supported farming models, Participatory Guarantee Schemes, and Farmer Producer Organisations (FPOs) can enable sustainable transitions. Sustainable lifestyles in a post COVID-19 world, as the cases show, require collective experimentation with producers that go beyond changed consumer behaviour to transform structures in food systems. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
ABSTRACT
The impact of the coronavirus disease 2019 (COVID-19) lockdown in the Hooghly estuarine region, India is assessed using the total suspended matter (TSM) concentration. The estimation of TSM is performed using Landsat-8/operational land imager (OLI), and an intercomparison of TSM load during the pre-lockdown and lockdown periods is done. It is observed that during the lockdown period, TSM reduced by 30-50%. This is a significant observation considering the ecological balance of the region and the fact that it is home to the largest mangroves in the world. This change in suspended matter presumably reflects the influence of reduction in anthropogenic activities owing to the COVID-19 lockdowns, such as industries, closure of shipping activities (through less dredging), and brick kilns (through less sediment removal), which are generally the primary contributors in this region. Even though these observed changes are representative of the positive influence of the COVID-19 lockdown, its implications in estuarine biogeochemistry still remain poorly quantified. The decrease in TSM content may increase light penetration, thereby increasing the primary productivity. In addition, low sediment load reaching the Bay of Bengal could influence the carbon export due to reduction in ballasting effect as reported from this region. In summary, the influence of the COVID-19 lockdown on the biogeochemistry of the aquatic ecosystem appears rather complex than thought earlier and may vary regionally based on local hydrodynamics. The analysis elucidates the complex interplay of regional lockdown and its implication in modulation of local biogeochemistry. However, the relative importance of each process in the Hooghly estuary remains to be fully evaluated.
ABSTRACT
The coronavirus pandemic has hit a hard blow on the world economy and employment rates. Countries like India, with a high population, have faced major economic degradation and high unemployment rates. Most of the countries are expected to face a major economic recession as most internal and external economic activities have ceased to operate due to the worldwide lockdown and quarantine measures being taken. This might affect the socioeconomic relationships between countries. It has also affected the economically challenged sector of the world largely. In India, about 41 lakh people lost their jobs, including several migrant workers. Several G7 countries have ensured subsidies as the jobless rates vary from 30million in the US to 1.76 million in Japan. © 2021, IFIP International Federation for Information Processing.
ABSTRACT
In today's world, COVID-19 pandemic has triggered unprecedented global health, humanitarian, socio-economic, and human rights crises. Across the globe, the pandemic has adversely affected a large number of people including children. It has multifaceted impacts on children including the psychological, mental, physical, social, and cultural. It has posed a serious threat to children's rights to survival and development and the highest attainable standard of health. The pandemic has also exposed children to an increased risk of experiencing physical and psychological violence, including maltreatment and sexual violence. The shutting down of schools and the decision of shifting traditional classroom to a digital platform is not only increasing learning inequality among children but also pushing a large number of children out of schools due to the digital divide. All these factors together have contributed towards the violation of the rights of children. The present paper, therefore, is an attempt to examine the extent of violation of child rights during the COVID-19 pandemic.For the present study, all relevant information is collected from the secondary sources of data.