ABSTRACT
Coronavirus disease-2019 (COVID-19) is a very serious health concern to the human life throughout the world. The Internet of Medical Things (IoMT) allows us to deploy several wearable Internet of Things-enabled smart devices in a patient's body. The deployed smart devices should then securely communicate to nearby mobile devices installed in a smart home, which then securely communicate with the associated fog server for information processing. The processed information in terms of transactions are formed as blocks and put into a private blockchain consisting of cloud servers. Since the patient's vital signs are very confidential and private, we apply the private blockchain. This article makes utilization of fog computing and blockchain technology simultaneously to come up with more secure system in an IoMT-enabled COVID-19 situation for patients' home monitoring purpose. We first discuss various phases related to development of a new fog-based private blockchain-enabled home monitoring framework. Next, we discuss how artificial intelligence-enabled big data analytics helps in analyzing and tracking the patients' information related to COVID-19 cases. Finally, a blockchain implementation has been performed to exhibit practical demonstration of the proposed blockchain system.
ABSTRACT
Emerging technologies, often defined as Industry 4.0, have been increasingly altering the development paths of businesses and individuals. As the pandemic continues to remain a part of our reality, we continue to improvise, innovate, and overcome. Technologies we have adapted today, are going to be an integral part of the post-pandemic world as well, this expedited technological adaption beckons the question—when we accepted the thrust of technology to the center of our lives, what the coming years bring forth remains to be seen, now that we as a species we have accepted our technologically interwoven existence. Disruptive technologies include artificial intelligence (AI), Blockchain, 3D printing, augmented reality, etc. While the speed and breadth of the fourth industrial revolution have been and will continue to be remarkable, the COVID-19 pandemic will accelerate innovation and catalyze technological changes already underway paving the way to Society 5.0. Society 5.0 aims to integrate human concerns back into the details of how we think about technologically advanced environments and promote user-friendly technologies for daily living;the initiative has also tried to address the challenges of productivity in an ageing society. This chapter presents examples of how emerging technologies have been employed during the COVID-19 crisis;discusses trends that may heighten the role of emerging technologies in a post-COVID-19 world;and reviews risks to and opportunities of faster adoption of emerging technologies and their implications to human security. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer 2022.
ABSTRACT
Since March 2020, the COVID-19 pandemic has significantly reduced the availability of global aircraft-based observations (ABOs), which has been restored later in 2021. This study focuses on the impact of ABOs on a regional reanalysis. Indian Monsoon Data Assimilation and Analysis (IMDAA) is a regional reanalysis for a period from 1979 to 2020 (originally up to 2018) over India and surrounding regions produced at the National Centre for Medium Range Weather Forecasting (NCMRWF), India, in collaboration with the UK Met Office. A comparison of the impact of ABOs on other conventional and satellite observations assimilated in the NCMRWF global model and IMDAA during 2019 and 2020 revealed the importance of ABOs, particularly in IMDAA, since it did not assimilate the latest satellite data as the IMDAA system was frozen in October 2016. A data denial experiment that removes all the ABOs from the IMDAA assimilation system for a period from March to November 2019 is designed. The results from the IMDAA reanalysis run, which assimilates ABOs during the same period, are compared with the data denial experiment. Assimilation of ABOs strengthened the upper tropospheric circulation, the Tropical Easterly Jet (TEJ), during the Indian summer monsoon compared to the data denial experiment. Analysis of the features of two cyclones that developed over the North Indian Ocean during the study period revealed that ABO assimilation played a key role in simulating the track and intensity of these cyclones when they were in the ‘severe' category. Since the sample is small, more cyclone cases need to be analysed to consolidate the result. © 2023 Royal Meteorological Society.
ABSTRACT
The rapid changes in socio-economic and environmental factors worldwide have resulted in natural and man-made disasters becoming increasingly difficult to manage. The emergence of systemic threats that are cross-border, complex, ambiguous, and uncertain in nature, such as the ongoing COVID-19 pandemic, has made traditional risk management methods inadequate. Many countries, including India, are struggling to address the challenges posed by these systemic vulnerabilities, leading to unresolved policy and governance problems. This research emphasises the need for comprehensive and proactive risk management methods that can address the challenges posed by systemic vulnerabilities, rather than reactive and fragmented approaches. The research identifies crucial turning points in India's disaster management history and explores the prospects for improved disaster risk governance in the country. The report underlines good advances in disaster administration in India, but it also emphasises the need for more systemic development in overall disaster risk management. The research identifies key design principles for India's systemic risk management that can help improve disaster risk management. This understanding can aid in developing effective frameworks, policies, and strategies to control systemic disaster risks and align with major international objectives such as the Sustainable Development Goals and the Sendai Framework for Disaster Risk Reduction. © 2023
ABSTRACT
Recent scholarship on refugee assistance has begun shifting the focus from traditional humanitarian interventions to alternative forms of refugee response, undertaken through more local, informal networks. These forms of humanitarian assistance and solidarity can transcend national refugee politics, and function as an alternative form of political engagement with the issue of refugee protection. Much of this emerging literature focuses on how host communities, diaspora networks, and activist groups support refugees. However, we know very little about how solidarities are generated between different refugee groups in the same host state, which has important implications for understanding the ways in which different refugee groups organise and seek to secure their own welfare in shared displacement. This study draws on evidence from India during the COVID-19 pandemic to analyze how such intergroup cooperation in exile develops. I find, first, that refugees navigate their vulnerabilities through informal, interpersonal forms of care and solidarity, which bolsters identification with shared challenges and goals. Second, such solidarities can generate collective action that helps refugees carve out certain spaces of inclusion in a larger context of structural exclusion. Third, the network structure of these solidarities is shaped by existing policy regimes and social relationships in key ways. © 2023 Informa UK Limited, trading as Taylor & Francis Group.
ABSTRACT
Data analysis and visualization are essential for exploring and communicating findings in medical research, especially in epidemiological surveillance. Data on COVID-19 diagnosed cases and mortality from crowdsourced website COVID-19 India Tracker, Census 2011, and Google Mobility reports has been used to develop a real-time analytics and monitoring system for the COVID-19 outbreak in India. We have developed a dashboard application for data visualization and analysis of several indicators to follow the SARS-CoV-2 epidemic using data science techniques. A district-level tool for basic epidemiological surveillance, in an interactive and user-friendly manner, which includes time trends, epidemic curves, and key epidemiological parameters such as growth rate, doubling time, and effective reproduction number, has been estimated. This demonstrates the application of data science methods and epidemiological techniques in public health decision-making while addressing the gap of timely and reliable decision-aiding tools. © Authors retain all copyrights.
ABSTRACT
Recurrent episodes of Hindu-Muslim violence have plagued India for many years. The Covid-19 pandemic poses new challenges against this backdrop of religious conflict. We explore the performance of various Indian districts in terms of their Covid-19 cases alongside their historical proclivities towards Hindu-Muslim violence. The link between inter-faith cooperation in an area and its subsequent performance in terms of Covid-19 cases is theoretically ambiguous. It may be that areas characterised by low(er) religious antagonism are better able to harness trust within and across communities to jointly battle the spread of the pandemic. Equally, the continual presence of religious violence may induce cooperation within each community and thus check the spread of the virus in riot-prone areas. Combining several district-level datasets, we uncover a robust correlation between various Covid-related outcomes and past conflict measures. Specifically, we find that higher levels of prior Hindu-Muslim conflict are associated with a lower number of Covid-19 infections, deaths and active cases. This pattern is consistent and holds across a wide range of specifications. Our empirical analysis therefore finds strong support for the latter channel. © 2022 selection and editorial matter, Rajib Bhattacharyya, Ananya Ghosh Dastidar and Soumyen Sikdar;individual chapters, the contributors.
ABSTRACT
Present-day higher education institutions offering undergraduate engineering programs need to prepare their students for a world filled with complex global challenges. Such preparation requires the acquisition of multidisciplinary knowledge and the application of multidisciplinary methodologies. A pilot initiative was launched in Fall 2017 for an elective three-year (sophomore to senior year) cross-departmental multi-disciplinary undergraduate engineering program at the Massachusetts Institute of Technology (MIT) named New Engineering Education Transformation (NEET). The program has three cross-departmental pathways across STEM disciplines and technical domains. The program has completed its fourth year of operation and has grown to become the fourth-largest undergraduate academic cohort at MIT. This paper is divided into two parts: the first part describes the revision of program requirements and their implementation during Fall 2019-Fall 2020. The second part describes the launching of a new single-themed program titled Climate & Sustainability Systems, which took place and was implemented during Summer-Fall 2021. Both initiatives responded to issues and changing circumstances raised by students, faculty, and instructional staff, with the aim of affording students more flexibility, reducing the additional workload beyond their chosen majors, enhancing their educational experience, and increasing their engagement with the program-wide community. In January 2020, following feedback collected from MIT students, faculty, instructional staff, and senior administration, we began a systematic process of reviewing the program's academic requirements. Data collected includes student questionnaires and specifications of program requirements throughout the study period. The revised requirements were published toward the end of the Spring 2020 semester, serendipitously around the same time as the COVID-19 mandated university-wide pivot from in-person on-campus teaching to emergency remote teaching and were implemented in Fall 2020. Since the publication of these new requirements, enrollment in the program has increased substantially year-on-year across all program threads. Subsequent data collection during Spring 2020 and Spring 2021 showed that word-of-mouth about the program has grown stronger, with 'current students' and 'other first-years' being two of the most-cited sources as to how first-years get to know about NEET. This paper explains the impetus for changing the program requirements, describes how the new requirements were formulated and implemented, and outlines what we have learned from implementing the revised requirements. We also describe how we collaborated with various stakeholders in the planning, design, and implementation of the revised requirements. For the second part of the paper, we describe how we launched a new climate and sustainability pathway based on our three-year experience of introducing pathways connected to energy, manufacturing ands materials, and sustainable development of cities, and on the growing interest amongst students in combating climate change in a sustainable manner. We describe how the process of consolidation was planned out and designed, how we collaborated with various stakeholders and how initial implementation has undergone. It should be emphasized that the approach we have taken here is largely qualitative and based primarily on how students and other key stakeholders responded to, engaged with the NEET program, and helped to evolve it. NEET leadership commissioned a systematic programmatic evaluation starting from Spring 2021, and we will be guided by their assessment of the changes as we look to the future. This paper is intended for institutional leadership, departmental leadership, faculty, and academic staff seized by the need to create and implement relevant and engaging cross-departmental multi-disciplinary undergraduate engineering programs. © American Society for Engineering Education, 2022.
ABSTRACT
Background: Comprehensive real-world evidence of the virulence of COVID-19 Omicron, Delta, and Alpha variants as well as the effectiveness of booster vaccinations in patients with cancer are lacking. We aimed to fill in these gaps for cancer patients and provide essential insights on the management of the fast-evolving pandemic by leveraging the nationally-representative electronic medical records from the National COVID Cohort Collaborative (N3C) registry. Methods: The virulence of COVID-19 variants was examined according to severe outcomes of infected patients with cancer, compared with non-cancer patients, using the N3C data between 12/01/2020 and 02/03/2022. Variants were inferred according to the time periods of variant dominance at > 95% accuracy. The Cox proportional hazards model was employed to evaluate the effects of COVID-19 variants, adjusting for age, gender, race/ethnicity, geographic regions, vaccination status, cancer types, smoking status, cancer treatments, and adjusted Charlson Comorbidity Index (CCI). Results: Our study cohort included 114,195 COVID-19 patients with cancer and 160,493 without cancer as control. Among them, 52,539 (21%) were infected by Omicron, 82,579 (33%) by Delta, and 115,200 (46%) by Alpha variants. Prior to the COVID-19 breakthrough infection, 7%, 22%, 3%, and 69% were vaccinated with 1 dose, 2 doses, a booster, or unvaccinated respectively. The proportions of hospitalization and death among patients with vs without cancer were 40% and 7% vs 18% and 0.4%, respectively. Characteristics of the cancer subcohort are summarized in the Table. Our analysis showed dramatically lower risks of severe outcomes for patients who were infected by Omicron (HR 0.42, 95%CI: 0.38 - 0.46) and slightly lower risks for Delta (HR 0.93, 95%CI: 0.89 - 0.98) compared with those infected by Alpha, after adjusting for other demographic clinical risk factors, and vaccination status. This trend remained similar in subgroups of patients with solid tumors, hematologic malignancies, or without cancer. Similar associations were observed when virulence was evaluated in association with mortality. The effectiveness of booster vaccinations varied across sub-cohorts stratified by variants and cancer types. Booster shots reduced the risk of severe outcomes for patients with solid tumors infected by Omicron variant or hematologic malignancies infected by Delta variants. Conclusions: Our work provides up-to-date and comprehensive real-world evidence of the virulence of COVID-19 variants in patients with cancer. Omicron variant showed significantly reduced virulence for different cancer types.
ABSTRACT
Background: Post-acute sequelae of SARS-CoV-2 or long COVID, is characterized by persistence of symptoms and/or emergence of new symptoms post COVID-19 infection. As evidence accumulates and national initiatives arise to address this increasingly prevalent syndrome, characterization of specific patient groups is still lacking including patients with cancer. Using a nationally representative sample of over 4.3M COVID-19 patients from the National COVID Cohort Collaborative (N3C), we aim to describe characteristics of patients with cancer and long COVID. Methods: We employed two approaches to identify long COVID patients within N3C: i) patients presenting to a long COVID clinic at four N3C sites and ii) patients diagnosed using the recently introduced ICD-10 code: U09.9 Post COVID- 19 condition, unspecified. We included patients with at least one positive COVID-19 diagnosis between 1/1/2020 and 2/3/2022. Patients had to survive at least 90 days from the date of their COVID- 19 diagnosis. Analyses were performed in the N3C Data Enclave on the Palantir platform. Results: A total of 1700 adult patients with long COVID were identified from the N3C cohort;634 (37.3%) were cancer patients and 1066 were non-cancer controls. The most common represented cancers were skin (21.9%), breast (17.7%), prostate (8.3%), lymphoma (8.0%) and leukemia (5.7%). Median age of long-COVID cancer patients was 64 years (Interquartile Range: 54-72), 48.6% were 65 years or older, 60.4% females, 76.8% non-Hispanic White, 12.3% were Black, and 3% Hispanic. A total of 41.1% were current or former smokers, 27.7% had an adjusted Charlson Comorbidity Index score of 0, 18.6% score of 1 and 11.2% score of 2. A total of 57.2% were hospitalized for their initial COVID-19 infection, the average length of stay in the hospital was 9.6 days (SD: 16.7 days), 9.1% required invasive ventilation, and 13% had acute kidney injury during hospitalization. The most common diagnosis among the non-cancer long COVID patients was asthma (26%), diabetes (17%), chronic kidney disease (12%), heart failure (9.4%), and chronic obstructive pulmonary disease (7.8%). Among long COVID patients, compared to non-cancer controls, cancer patients were more likely to be older (OR = 2.4, 95%CI: 1.1-5.4, p = 0.03), have comorbidities (OR = 4.3, 95%CI: 2.9-6.2, p < 0.0001), and to be hospitalized for COVID-19 (OR = 1.3, 95%CI: 1.0-1.7, p = 0.05), adjusting for sex, race/ethnicity, body mass index and smoking history. Conclusions: In a nationally representative sample of long COVID patients, there was a relative overrepresentation of patients with cancer. Compared to non-cancer controls, cancer patients were older, more likely to have more comorbidities and to be hospitalized for COVID-19 warranting further investigation to identify risk factors for long COVID in patients with cancer.
ABSTRACT
Background: Patients with multiple myeloma (MM), an age-dependent neoplasm of antibody-producing plasma cells, have compromised immune systems due to multiple factors that may increase the risk of severe COVID-19. The NCATS' National COVID Cohort Collaborative (N3C) is a centralized data resource representing the largest multi-center cohort of ∼12M COVID-19 cases and controls nationwide. In this study, we aim to analyze risk factors associated with COVID-19 severity and death in MM patients using the N3C database. Methods: Our cohort included MM patients within the N3C registry diagnosed with COVID-19 based on positive PCR or antigen tests or ICD-10-CM. The outcomes of interest include all-cause mortality (including discharge to hospice) during the index encounter, and clinical indicators of severity (hospitalization/ED visit, use of mechanical ventilation, or extracorporeal membrane oxygenation/ECMO). Results: As of 09/10/2021, the N3C registry included 690371 cancer patients, out of which 17791 were MM patients (4707 were COVID-19+). The mean age at diagnosis was 65.9yrs, 57.6% were >65yo, 46.4% were females, and 21.8% were Blacks. 25.6% had a Charlson Comorbidity Index (CCI) score of ≥2. 55.6% required an inpatient or ED visit, and 3.65% required invasive ventilation. 11.4% developed acute kidney injury during hospitalization. Multivariate logistic regression analysis showed histories of pulmonary disease (OR 2.2;95%CI: 1.7-2.8), renal disease (OR 1.8;95%CI: 1.4-2.4), and black race (p<0.001) were associated with higher risk of severity. Interestingly, smoking status was significantly associated with a lower risk of severity (OR 0.7;95%CI: 0.5-0.9). Further, protective association was also observed between COVID-19 severity and blood or marrow transplant (BMT) (OR 0.52;95%CI: 0.4-0.7), daratumumab therapy (OR 0.64;95%CI: 0.42- 0.99) and COVID-19 vaccination (OR 0.28;95%CI: 0.18-0.44). IMiDs were associated increase in the risk of COVID-19 severity (OR 2.1;95%CI: 1.6-2.7). 2.3% of N3C-myeloma COVID-19+ patients died within the first 10 days, while 4.95% died within 30 days of COVID-19 hospitalization. Overall, the survival probability was 90.5% across the course of the study. Multivariate cox proportional hazard model showed that CCI score ≥2 (HR 4.4;95%CI: 2.2-8.8), hypertension (HR 1.6;95%CI: 1.02- 2.4), IMiD (HR 2.6;95%CI: 1.8-3.8) and proteasome inhibitor (HR 1.6;95%CI: 1.1-2.5) therapy were associated with worse survival. COVID-19 vaccination (HR 0.195;95%CI: 0.09-0.45) and BMT (HR 0.65;95%CI: 0.4-0.995) were associated with lower risk of death. Conclusions: We have identified previously unpublished potential risk factors for COVID-19 severity and death in MM as well as validated some published ones. To the best of our knowledge, this is the largest nationwide study on multiple myeloma patients with COVID-19.
ABSTRACT
The rapid development of technology in artificial intelligence (AI) includes face recognition. Some of these developments in face recognition systems can be found easily in everyday life, such as entry access, payment, or attendance recording systems. Notably, the demand for image-based face recognition methods to record attendance increases due to its effectiveness and efficiency. Compared to conventional methods like fingerprint or RFID, facial recognition offers better results. On the other hand, with the arising needs in dealing with the novel coronavirus (COVID-19) pandemic, official health protocols require mask-wearing and maintaining a minimum distance of 1 meter between individuals to prevent the spread of the virus. Along with such a situation, a face recognition system to record attendance can reduce the occurrence of direct contact and allow each individual to maintain a safe distance, including from the attendance device. This paper presents the performance of a masked-faced recognition system that implemented SSD (Single Shot Detection) and ResNet feature extraction. The face recognition system application developed using Python and related libraries show a stable level of masked face recognition accuracy. This evaluation was made at predetermined distances between the face and the camera and measured at room lighting of 200 lux with an average accuracy of 67%. The application also has a feature to send notification emails to every employee who is unable to attend work on their scheduled workdays. © 2021 IEEE.
ABSTRACT
Background: In this coronavirus disease-2019 (COVID-19) pandemic, safe and effective preventative vaccines are essential to contain the pandemic, which has had severe medical, economic, and societal consequences, despite some people still becoming infected after receiving immunisation. Methods: A total of 200 patients were examined and split into two groups: (1) 100 consecutive COVID-19-positive cases who had been vaccinated and (2) 100 consecutive COVID-19-positive patients with no vaccination. At the time of the scan, the patient's vaccination status was noted. Results: The computed tomography severity score (CTSS) of unvaccinated individuals was found to be considerably greater than that of partly or fully vaccinated patients (median 13 vs. 7, P < 0.001). Completely vaccinated individuals had a considerably lower median CTSS than partly vaccinated patients (6 vs. 9, P < 0.001). Conclusions: Individuals should be thoroughly vaccinated to avoid major lung disease. As a result, stronger dedication and motivating efforts should be made worldwide to improve the COVID-19 vaccination program.
ABSTRACT
Since time immemorial, natural products have found applications for the treatment of many maladies and ailments. This review attempts to portray the chemistry, bio-medical activities and efficacy in the therapy of COVID-19 of one age-old famous spice cumin and an eminent herb turmeric. Both of them have found significant applications in the disciplines of Ayurveda for their physiological and nutraceutical benefits. It is worth mentioning that the versatility of cumin and turmeric in terms of treating a variety of ailments, as well as their antioxidant properties, has always piqued the curiosity of the scientific community. Even in recent times, both of them are being scrutinised for their response to the global pandemic, COVID-19. Thymoquinone, the most abundant constituent of black cumin has shown immense therapeutic potential. On the other hand, curcumin, an important bio-active component of turmeric owns a wide array of pharmacologic effects. The utilisation of curcumin-derived carbon quantum dots and nanoparticle bound curcumin, on the other hand, is a new field in nanomedicine. Having said all these things, a few challenges have been encountered regarding their uses as drug candidates. Several research papers for the past fifteen years have been consulted using online databases like Google Scholar, Scopus and PubMed to compose this article in the viewpoint of the promises and challenges of the active constituents of cumin and turmeric as potential drug candidates.
ABSTRACT
Before Covid the launch of 'Digital India' program set the ground for competition in the field advance technology across India. The launch of 4G technologies made Indian telecom sector a booming market for advanced technology. With growing demand for technical education the demand for technically advanced educational goods also increased in India before 2020. During 2020 social distancing education became online. This increased the demand for digitalized educational goods by teachers working from home and students learning online. With pay cut, accompanied by low production both parties could not afford the rising price of digitalized educational goods. This paved way for search of less priced (or even free) yet qualitative digitalized educational goods like ebook. Post pandemic experts believe that the market for e-book is going to rise worldwide. Keeping this in mind the paper makes a modest attempt to critically appreciate the top e-book a market of the world visa vies India with special reference to Odisha. © The Electrochemical Society
ABSTRACT
The COVID-19 pandemic has put a major impact on the children in conflict with law. This paper aims to capture the major key issues and the necessary steps taken by the state government to mitigate the negative consequences for children in conflict with law. It is found from various studies that during the beginning period of COVID-19, reduction in the rate of crime is observed, and the reason for this is due to drastic change in the routine activities of the individuals and with the closure of schools, colleges and other services. The pandemic effect of staying at home and social distancing led to decline in crime rate by the juveniles. The most concerning part was the staffing crisis seen at observation and special homes in the state of Odisha. The social factors such as parental supervision, love, care and affection play vital role against delinquent behaviour of children. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
ABSTRACT
Background: Atypical category of COVID-19 could not be differentiated from tuberculosis (TB) in high-resolution computed tomography (HRCT) of the chest because of similar imaging features. This study aims to distinguish between the HRCT features of TB and atypical COVID-19. Methodology: Interferon-gamma release assay (IGRA) was performed in all the 54 COVID-positive patients, showing atypical COVID features that are suspicious of TB on the HRCT chest. Atypical imaging features such as a tree in bud nodules, patchy consolidations, cavitation with surrounding consolidation, discrete nodules, mediastinal lymphadenopathy, and pleural effusion were analyzed in 50 IGRA-negative patients. Results: We found trees in bud nodules (93%) and consolidations (56%) involving predominantly lower lobes, i.e., superior and posterobasal segments. Discrete nodules and cavitation with surrounding consolidation were seen involving predominantly upper lobes (78 and 57% cases, respectively), i.e., apicoposterior and lingular segments of the left upper lobe. The maximum number (67%) of right paratracheal enlarged nodes and bilateral pleural effusions (71%) were found in IGRA-negative COVID-19 patients. Conclusions: It is not always possible to differentiate features of atypical COVID-19 from TB based on HRCT chest alone because of similar appearances and distribution of tree in bud nodules, consolidation, cavitation, and lymphadenopathy in HRCT chest. Since both bilateral and unilateral pleural effusions may be seen in TB, it is impossible to differentiate COVID-19 from TB based on pleural effusion. Therefore, exclusion of TB will need supportive, relevant laboratory investigations (Sputum acid fast bacilli, cartridge-based nucleic acid amplification test, and IGRA) for appropriate diagnosis and management.
ABSTRACT
Objective: To investigate the psychological strain experienced by orthopaedic healthcare workers and attributing factors during the COVID-19 pandemic. Methods: We surveyed healthcare workers in an outpatient musculoskeletal clinic with a questionnaire based on the validated Caregiver Strain Index1 . We have previously shown the prevalence of psychological strain during the pre-peak phase of the pandemic.2 The same group was followed up 6 months later (post-peak phase) to evaluate the downstream psychological strain. Results: Of the 57 participants who were followed up, 30 (52.6%) experienced a greater level of strain (Group A), similar to the prevalence in our previous study (51.6%). Significantly, participants in Group A work longer hours per day compared to Group B (8.8±1.6 vs. 8.0±1.5;mean difference, 0.8;95%CI, 0.0 to 1.6;p=0.043). “Family adjustments” (84.2%) garnered the most positive responses. Conclusion: Longer working hours contributes to psychological strain and measures should be designed to mitigate this. Potential impact on family life of healthcare workers is an unrecognised toll which should receive more attention. Health ministries should ensure measures to safeguard the mental health of healthcare workers to avoid reactive strategies during a pandemic. (Figure Presented).
ABSTRACT
In current situation, Coronavirus Disease-2019 (COVID-19) becomes a very serious health concern to the human life throughout the world. The Internet of Medical Things(IoMT) allows to deploy several wearable Internet of Things(IoT) enabled smart devices in a patient's body. The deployed smart devices should then securely communicate to nearby mobile devices installed in a smart home, which then securely communicate with the associated Fog server for information processing. The processed information in terms of transactions are formed as blocks and put into a private blockchain consisting of cloud servers. Since the patient's vital signs are very confidential and private, we apply the private blockchain. This article makes utilization of Fog computing and blockchain technology simultaneously to come up with more secure system in an IoMT-enabled COVID-19 situation for patients' home monitoring purpose. We first discuss various phases related to development of a new Fog-based private blockchain-enabled home monitoring framework. Next, we discuss how Artificial Intelligence(AI)-enabled Big data analytics helps in analyzing and tracking the patients' information related to COVID-19 cases. Finally, a blockchain implementation has been performed to exhibit practical demonstration of the proposed blockchain system. IEEE
ABSTRACT
As the first pandemic wave of Covid-19 hit India and the Indian Government announced a nationwide lockdown in March 2020, [1] it left millions of interstate migrant workers/daily-wage earners without a source of livelihood. Many of them had to return to their hometowns, often hundreds of kilometers away, with many walking a major part or the entirety of the distance [2]. This situation called for a design that could empower migrant workers to thrive in the new normal. An app was designed to enable migrant workers to search for jobs, learn about micro-entrepreneurship, get mental health assistance and develop skills. In terms of usage and interaction, this app proposes several unique features - a result of researching about and understanding our target demographic. At the core of the interaction is a chatbot. It asks questions to the user through text and audio, but the user replies through clickable buttons only. This makes it easy, even for those who may not be literate. The keyboard has been removed from this app. Another unique feature is the usage of interactive videos to explain various things, such as micro-entrepreneurship, skill training, etc. These videos make use of buttons for the users to make choices. In this paper, we discuss these two new interactions that will introduce a visual input method and an interactive video interface which will be inclusive of those less-privileged in terms of literacy in our ever-expanding tech world. The contents of the paper are targeted toward helping the next billion users. However, some of the things proposed may go on to improve interactions for all. © 2021, Springer Nature Switzerland AG.