Performance, Trust, or both? COVID-19 Diagnosis and Prognosis using Deep Ensemble Transfer Learning on X-ray Imagesg±

The COVID-19 pandemic still affects most parts of the world today. Despite a lot of research on diagnosis, prognosis, and treatment, a big challenge today is the limited number of expert radiologists who provide diagnosis and prognosis on X-Ray images. Thus, to make the diagnosis of COVID-19 accessible and quicker, several researchers have proposed deep-learning-based Artificial Intelligence (AI) models. While most of these proposed machine and deep learning models work in theory, they may not find acceptance among the medical community for clinical use due to weak statistical validation. For this article, radiologists' views were considered to understand the correlation between the theoretical findings and real-life observations. The article explores Convolutional Neural Network (CNN) classification models to build a four-class viz. "COVID-19", "Lung Opacity", "Pneumonia", and "Normal"classifiers, which also provide the uncertainty measure associated with each class. The authors also employ various pre-processing techniques to enhance the X-Ray images for specific features. To address the issues of over-fitting while training, as well as to address the class imbalance problem in our dataset, we use Monte Carlo dropout and Focal Loss respectively. Finally, we provide a comparative analysis of the following classification models - ResNet-18, VGG-19, ResNet-152, MobileNet-V2, Inception-V3, and EfficientNet-V2, where we match the state-of-the-art results on the Open Benchmark Chest X-ray datasets, with a sensitivity of 0.9954, specificity of 0.9886, the precision of 0.9880, F1-score of 0.9851, accuracy of 0.9816, and receiver operating characteristic (ROC) of the area under the curve (AUC) of 0.9781 (ROC-AUC score). © 2022 ACM.

Equity, social justice and transformational giftedness: A gifted academy in a vulnerable community

Given that uncertainty has become the signe des temps for our students in the current Covid-19 climate, one can pose the question: what types of skills would be relevant for the current and the next generation of students that would help them make sense of the changing world? School curricula and testing still anchored in the traditional mode of the 3Rs has resulted in a cadre of gifted students who have performed well academically but who have not been educated to reflect on using their "gifts" to transform society in just and meaningful ways. As opposed to being purely speculative on what transformative giftedness could be, we describe the genesis of a gifted academy- a school within a school situated within an impoverished community grounded in the principles of equity, social justice, and transformational giftedness. In this academy, the curriculum based on both socio-emotional learning (SEL) and problem-based learning (PBL), in tandem with interdisciplinary projects, provides avenues for the potential to transform students into making sense of uncertainty in the changing world in meaningful ways. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022. All rights reserved.

Nanomaterial-based smart coatings for antibacterial, antifungal, and antiviral activities

Infectious diseases caused by different pathogens (parasites, protozoa, bacteria, viruses, and fungi) have affected the world at various times in the form of epidemics and pandemics. The coronavirus has also directly affected the world's economy and public health. Various drugs such as antibiotics, antimicrobials, antifungals, and antivirals have been investigated to combat these diseases. However, these fatal infections are still a major concern because of their transmission through contaminated surfaces, human-to-human contact, airborne diffusion, and microbial resistance. Therefore, considerable efforts are required to suppress the transmission of these pathogens. Smart coatings are able to sense their environment and adapt their properties according to the stimulus. Furthermore, various parameters of coating technology can be controlled on a molecular level to influence the morphology. Nanomaterial (NM)-based smart coatings are 99.99% effective against bacteria, viruses, and fungi because of the unique properties of NMs involved. Moreover, NM-based smart coatings are 1000-fold more efficient than traditional coating technologies. Besides their antifungal, antiviral, and antibacterial application, they are anticorrosive and self-cleaning. This chapter summarizes various NM-based smart coatings (organic, inorganic, and carbon) implemented in antibacterial, antifungal, and antiviral applications. Furthermore, the application of these coatings in various fields and their associated challenges will be discussed. © 2023 Elsevier Inc. All rights reserved.

Applications of Machine Learning in Viral Disease Diagnosis

Viral diseases are common and natural in human it spreads from animals and other humans. It seeks to identify the proper, reliable, and effective disease detection as quickly as possible so that patients can receive the right care. It becomes vital for medical field searches to have assistance from other disciplines like statistics and computer science because this detection is frequently a challenging process. These fields must overcome the difficulty of learning novel, non-traditional methodologies. Because so many new techniques are being developed, a thorough overview must be given while avoiding some specifics. In order to do this, we suggest a thorough analysis of machine learning which is used for the diagnosis of viral diseases caused in humans as well as plans. Predictions are made which is not obvious at the first glance does machine learning will be more helpful in making decisions. The study focuses on the machine learning algorithms for diagnosis of viral diseases for early diagnosis and treatment of viral diseases with greater accuracy. The work helps the researchers and medical professionals for learning and to give treatment for determining the applications of different machine learning techniques run to evaluate the parameters. Through examination of various parameters new machine learning model is proposed understanding the applications of machine learning in viral disease diagnosis like imaging techniques, plant virus diagnosis and the solution for the problem, Covid 19 diagnosis. © 2023 Bharati Vidyapeeth, New Delhi.

Missed and Lost to Follow-up Cases in HIV Positive Patients and the Impact of Lockdown During COVID-19 Pandemic on Adherence to Anti-retroviral Therapy at ART Center, Jhansi, Uttar Pradesh

Background: Anti-retroviral therapy (ART) for HIV has changed a highly fatal disease to a chronic manageable condition. National technical guidelines by NACO say that adherence of >95%(optimal) is required for optimal viral load suppression which is a challenge both for the patient and the health system.Objectives: This study was conducted to determine the reasons for missed and lost to follow-up (LFU) cases and to assess the impact of the COVID pandemic on ART adherence.Settings and Design: Cross-sectional study conducted at ART center, Jhansi.Methods and Material: 357 patients were administered a self-designed questionnaire after taking informed consent to enquire about the reasons for missing doses and LFU and whether they missed treatment during the lockdown.Statistical analysis used: the results were expressed in frequencies and percentages and appropriate statistical tests were applied.Results: 72% HIV patients had optimal adherence and 6.7% were on second-line treatment. Out of 357 patients, 56 had missed treatment and 10 were LFU. The main reasons for the missing were run out of pills, busy with other things and being away from home. The number of episodes of missed and LFU increased during the pandemic. The main problems faced were lack of transport (24), fear of catching the disease (7), no money to hire a vehicle (5).Conclusions: Constant monitoring and handholding of those with suboptimal adherence is required. Travel allowance to such patients and regular counseling will help to ensure adherence. Long-term solutions include vocational rehabilitation and awareness programs to reduce stigma and discrimination.

Antibody Response to SARS-COV-2 Vaccination in Healthcare Workers of Uttarakhand: A Prospective Follow-up Study

Background: Countries around the world are now racing to vaccinate people against SARS-CoV-2, the virus that causes COVID-19. The Government of India also rolled out its vaccination drive from 16th January '2021. Aims: To estimate the antibody response of the COVID-19 vaccine in the form of SARS-COV-2 IgG antibodies in vaccinated healthcare workers.Methods: Prospective follow-up was study conducted on healthcare workers (HCWs) of a Medical college in Dehradun, Uttarakhand. Healthcare workers who have been vaccinated for COVID-19 were tested for SARS-CoV-2-IgG antibodies at regular intervals i.e at 4 weeks after the 1st dose and then again at 4 weeks after the 2nd dose. The third sample was taken 6 months after the 2nd dose. Results: A total of 302 HCWs were enrolled in the study who gave their samples for IgG antibody estimation after the Covishield vaccine. After 4 weeks of completion of both doses, 96% HCWs formed SARS-COV-2 IgG antibodies, whereas 4% didn't. Then after 6 months of follow-up, 14% HCWs have become negative for antibodies and better immunity is seen in people who also got infected with COVID-19 during this time.Conclusion: This study concludes that the immunity gained after vaccination is waning off in around 6 months and there is a need for a booster dose, especially for people at high risk. The infection control practices still play a crucial role in the prevention of this deadly disease.

Severe Acute Hepatitis: An Emerging Grave Illness in Children

Acute hepatitis of unknown origin in children has been recently described in the literature, and a case definition has also been proposed for this condition. The exact etiology is unknown and exclusion of infectious, metabolic, autoimmune and toxin mediated injuries is essential. Management for this condition is supportive, but some may require liver transplantation. Infection prevention and control practices are important as the etiology remains unidentified.Copyright © 2023, Indian Academy of Pediatrics.

Smart Device for CO2 Measuring and Supplementing with O2 Using IOT

Globally, atmospheric carbon dioxide (CO2) concentration is rising due to rising carbon-based fuel consumption and ongoing deforestation. As carbon dioxide levels grow due to the warming trend, the atmosphere's temperature is predicted to climb. Increased fatigue, headaches, and tinnitus are just a few health issues that high CO2 concentrations in the atmosphere can cause. The electrical activities of the brain, the heart, and the lungs have all been demonstrated to change significantly after a brief exposure to 0.1 percent CO2. Continuous measurements of the atmospheric CO2 content have recently been shown to help evaluate the ventilation conditions in buildings or rooms. Additionally, it prevents the development of the severe acute respiratory syndrome coronavirus 2 (Severe acute respiratory). The coronavirus, known as a powerful acute respiratory, can make people ill. This has grown to be a significant concern in emergency medicine. © 2022 IEEE.

Bridging the gap in access to filter paper monitoring in patients with phenylketonuria - a Medical Nutrition Therapy for Prevention (MNT4P) program initiative

Background: Filter paper (FP) or dried blood spot testing is the preferred method of monitoring blood levels of phenylalanine and tyrosine for patients diagnosed with phenylketonuria (PKU) in the state of Georgia. This cost effective and convenient at-home approach simplifies the nutritional assessment and management of patients with PKU and lessens the burden on patients and caretakers. Emory and a local specialty laboratory had a long-standing contract for FP testing, which included patient insurance and grant billing. When this laboratory abruptly ended FP testing in September 2020, an emergent alternative plan became essential to prevent potential disruptions in patient care while working on a sustainable solution for PKU monitoring, especially given the ongoing COVID-19 pandemic. Method(s): Emory's in-house laboratory was not contracted with outside laboratories to process FP testing and bill insurance. To mitigate any delays in FP testing, the MNT4P program conducted a vendor search and selected ARUP Laboratories to perform PKU FP testing. Eligible patients included those referred, enrolled, and consented to the MNT4P program. To streamline the FP submission process, customized FP cards and business reply envelopes were developed and distributed in collaboration with PerkinElmer, Emory Mail Services and the United States Postal Service. Patient outreach efforts were facilitated through email campaigns, MNT4P website updates, and in collaboration with Georgia PKU Connect. Result(s): 95 patients were referred to MNT4P program for FP paper monitoring. During the 4-month period, a total of 239 FPs were collected from patients with PKU and processed with corresponding results reported to Emory Clinic, allowing registered dietitians to continue nutrition management without disruption. Once the patient-centered business prototype was established, FP testing was successfully transferred from the MNT4P program to Emory's inhouse laboratory. FP testing is now a part of Emory's test catalog, and results are available to providers through electronic health records. Conclusion(s): The MNT4P program successfully worked with Emory's in-house laboratory to develop a sustainable solution for FP monitoring. It prevented interruption in long-term follow up of patients with PKU. MNT4P continues to be the payor of FP tests for uninsured and underinsured patients.Copyright © 2022 Elsevier Inc. All rights reserved.

Execution of Market Basket Analysis and Recommendation Systems in Physical Retail Stores to Advance Sales Revenues

In the early months of 2020, pandemic covid-19 hit many parts of the world. Especially developing countries like India observed a negative growth rate in few quarters of last financial year. Retailing is one of the key sectors that contribute to Indian GDP with a share of nearly 10 percent. Hence there is a need for the retail sector to bounce back which is possible with the efficient use of new digital technologies. Market basket analysis is used here to extract the association rules which can be directly used for formulating discount and combo offers. Along with that, these rules can be used to decide the product positioning in the retail store. Items which are bought together can be placed next to each other to increase sales. Recommendation systems are most commonly used in ecommerce websites like Amazon, Flipkart, etc, and streaming platforms like Netflix to recommend the items that are to be purchased by users. Although recommendation engines are implemented in multiple web and mobile applications, these are not in the implementation stage in offline retail stores due to many implications associated with them like infrastructure, cost, etc. In this project, we have used market basket analysis and recommendation systems to propose a model to implement in retail stores to increase sales revenues and enhance customer experience. © 2022 IEEE.

Marching Ahead through the Pandemic: Continuing Anesthesia Services in COVID Era-Our Experience from a Tertiary Care Cancer Centre

Introduction This paper aims to provide an overview of the administrative and clinical preparations done in a tertiary care cancer hospital in continuing operation theatre (OT) services through the COVID pandemic.Methods Retrospective data collection, data for the past 1.5 years (COVID period) March 2020 to August 2021 were compared to surgical output for a similar duration of time before the COVID era (September 2018-February 2020).Results A total of 1,022 surgeries were done under anesthesia in the COVID period as against 1,710 surgeries done in a similar time frame in the pre-COVID era. Overall, we saw a 40%drop in the total number of cases. Thorax, abdominal, and miscellaneous surgeries (soft tissue sarcomas, urology, and gyneconcology) saw a maximum fall in numbers;however, head and neck cases saw an increase in numbers during the pandemic. Surgical morbidity and mortality were similar in the COVID and pre-COVID era. No cases of severe COVID infection were reported among the healthcare staff working in OT.Discussion We could successfully continue our anesthesia services with minimal risk to healthcare staff throughout the pandemic by adopting major guidelines in a pragmatic and practical approach with minor changes to suit our setup.

A Real-Time Analytic Face Thermal Recognition System Integrated with Email Notification

COVID-19 is a contagious disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The disease has spread worldwide, leading to an ongoing pandemic. The most common symptom of COVID-19 is fever which can be detected using various manual screening techniques that have the risk of exposing the personnel. Since the virus has globally spread, a reliable system to detect COVID-19-infected people, especially before entering any premises and buildings, is in high demand. The most common symptom that can be detected is fever, even though people with fever might not have COVID-19. Thus, a real-time analytic face thermal recognition system integrated with email notification that has the capability to scan the person's temperature and simultaneously analyze the measured temperature with the recorded/stored information/data is presented in this paper. The proposed system is also able to send an email notification to the relevant authorities during the real-time analytical process. Besides that, this information is also recorded in the system database for continuous monitoring of the respective person's health status. The development of the proposed system is integrated with a Thermal Module AMG8833, Pi camera, and Raspberry Pi Zero Wireless. The proposed system has been tested and the captured results successfully accomplished the development objectives.

Pilgrim Experience and Revisit Intention Post COVID-19: An Exploratory Study of Amarnath Holy Shrine in Jammu and Kashmir, India

The present preliminary study seeks to explore the pilgrim tourist experience and their revisit intentions post-COVID pandemic. Data were collected at the Amarnath Holy Shrine in Jammu and Kashmir, India, using a combination of primary and secondary sources, including seven in-depth semi-structured telephone interviews with pilgrims using the snowball sampling technique. Five main themes were found regarding revisiting intentions, including ease of registration, safety and security, health facilities, accommodation facilities, food and beverage services, and connectivity of the destinations. The study highlights that despite COVID-19, most respondents agree to return to visit the holy Amarnath Cave as they felt that they would not be affected by COVID-19 in the future. This research has implications for tourists' safety and security concerns at religious sites, including highlighting the need for proper infrastructure development to enhance the sustainability of religious destinations. Regarding social impacts, the local administration must make efforts to carry out the pilgrimage in a sustainable way post-COVID-19, following new procedures to ensure the safety and health of the tourist and the local community. The Shrine board and Local administration must formally implement these standards via formal Standards of Procedure (SOP). © 2022 International Journal of Religious Tourism and Pilgrimage.

AI Enabled Smart Campus for Health Safety and Monitoring

COVID 19 has had a major effect on society. In order to keep people's spacing, new requirements have been placed in place regarding the amount of users authorized in individual rooms in offices, shops, etc. Along with social distance, regular temperature verification at mall entrances are indeed permitted. An excellent embedded machine learning system is proposed in this work to identify face masks automatically and detect the body's temperature in a real-time application. The proposed system, in particular, utilizes a raspberry pi camera to capture real-time video simultaneously by identifying face masks with the help of a classification technique. The face mask detector is constructed by utilizing mobilenetv2 and imaging net pre-trained weights to consider three scenarios: wearing a mask correctly, wearing a mask incorrectly, and not wearing any at all. By placing a temperature gauge on a Raspberry Pi, a framework has also been developed for determining a person's body temperature. The numerical outcomes show the feasibility and performance of our integrated devices in compared to many cutting-edge research. This temperature and facemask detection device monitors a person's body heat and detects whether or not that person is wearing a facemask. Consequently, any organization's entrance could contain this device. In this study, the door is only released if the temperature is below 99° F, which would be calculated by the Electro Selective Pattern-32 images, the MLX sensor, and the fact that a person's face is 80% protected by a facemask. © 2023 IEEE.

Decision-Making in Mask Disposal Techniques Using Soft Set Theory

In 2003, Maji, Biswas, and Roy developed a method for applying soft set theory to a decision-making problem using Pawlak's rough set approach. Further, research proved that Maji's soft set reductions were inaccurate in 2005, leading to the development of a new method by Chen et al. This article applies soft theory to waste management and disposal decision-making problems. The excessive masks discarded during the COVID-19 era, in particular, must be managed effectively, and the current paper provides a method for better decision-making of the same. The algorithms used are first to compute the reductions and then the reduct soft set is used to choose the ideal objects for decision problems, and then the choice value is calculated. Predefined parameters are sometimes not enough to make precise decisions to solve general or real-time issues. Therefore, additional parameters are added into the existing set, either as a new parameter or generated by the handling of existing ones. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

A review on synthetic methods for 2-Deoxy-D-glucose

2-Deoxy-D-glucose (2-DG) is a non-metabolizable glucose analog that has shown promising pharmacological activities and has been used to study the role of glucose in cancer cells. 2-DG is an inhibitor of glycolysis, potential Energy Restriction Mimetic agent and inhibits pathogen-associated molecular patterns. Its radioisotope derivatives have application as tracers. Recently, 2-DG has been used as an anti-COVID-19 drug lowering the need for supplemental oxygen. In this review, different synthetic strategies for preparation of 2-DG including enzymatic synthesis have been discussed. The understanding of these methods would help in developing therapeutics or diagnostic agents aimed at exploring therapeutic targets related with energy metabolism. © AUTHOR(S).

Intelligent demand and supply of electrical vehicles

Today, it is impossible for a being to fathom living without a vehicle for transportation of any kind. All governments have put into place numerous regulations on the use of well-maintained vehicles to stop this, but there are always exceptions. The world is transitioning to an automated system that requires little human interaction as a result of the COVID-19 pandemic. This intelligent system involves identifying the predicting trends in the market analysis based on the expenses and revenues patterns of a particular group of people in a city or location. This the system needs to offer the insight into the direction for decision making of the market on demand of vehicle as per the supply with respect to the type of roads, season, and gender and age group patterns. Apartfrom this, the tax rules make a huge difference in this context. In some zones, many governments encourage by giving lots of subsidies or give tax relaxations to certain cc vehicles. In the future, based on the distance travelled, the system can impose tax fair or toll plaza fee. © 2023, IGI Global. All rights reserved.

Controlling of Fake Information Dissemination in Online Social Networks: An Epidemiological Approach

Due to the fast advancement of Internet technology, the popularity of Online Social Networks (OSN) over the Internet is increasing day by day. In the modern world, people are using OSN to communicate with others around the world who may or may not know each other. OSN has become the most convenient means to transmit media (news/content) and gather or spread information in the world. The posts (contents) on OSN affect and impact people, and minds at least for some time. These contents are important because they play a crucial role in taking the decision. The posts which are available on the OSN may be information or just misinformation. The misinformation may be a type of fake news or rumour. This is very difficult for people to differentiate whether the posts are information or rumour. Therefore, the development of techniques that can prevent the transmission of false information or rumours that might harm society in any way is critical. In this paper, a model is developed based on the epidemic approach, for examining and controlling fake information dissemination in OSN. The proposed model illustrates how different misinformation debunking measures impact and how misinformation spreads among different groups. In this article, we explain that the proposed model will be able to recognize and eradicate fake news from OSN. The model is written as a system of differential equations. Its equilibrium and stability are also carefully examined. The basic reproduction number $(R_{0})$ is calculated, which is an important parameter in the study of message propagation in OSN. If $R_{0} < 1$ , the propagation of rumor in the OSN will be minimal;nevertheless, if $R_{0} > 1$ , the fake information/rumor will continue in OSN. The effects of disinformation of rumours in OSN in the real world are explored. In addition, the model covers the fake information/rumour dissemination control mechanism. The comparative study shows that the proposed model provides a better mechanism to prevent the dissemination of fake information in OSN in comparison to other previous models Extensive theoretical study and computation analysis have also been used to validate the proposed model © 2013 IEEE.

Vaccine politics: Law and inequality in the pandemic response to COVID-19

International mechanisms failed to achieve equitable distribution of COVID-19 vaccines—prolonging and deepening the pandemic. To understand why, we conduct process tracing of the first year of international policymaking on vaccine equity. We find that, in the absence of a single venue for global negotiation, two competing law and policy paradigms emerged. One focused on demand and voluntary action by states and firms, while the alternative focused on opening knowledge and expanding production through national and international law. While these could have been complementary, power inequalities between key actors kept the second paradigm from gaining traction on the global agenda. The failure of the prevailing policy paradigm to secure equity is explained, not by unforeseen technical and financing challenges as some suggest, but by a fundamental misalignment with the political environment. While norm entrepreneurs encouraged sharing, political incentives pushed governments towards securing and hoarding doses. Firms responded to the latter. Mechanisms like COVAX proved incapable of countering these predictable international and domestic political forces. Earlier funding would not likely have changed the behaviour of states or firms in the absence of legal commitment. Barring significant geopolitical changes, a shift to include open/supply-focused policies will be necessary to achieve equity in future pandemics. © 2023 Durham University and John Wiley & Sons Ltd.