The Impact of the COVID-19 Pandemic on Female Domestic Workers in an Urban Setting

This paper focuses on the increasing marginalisation and vulnerability of female domestic workers in urban India in the wake of the COVID-19 pandemic. It analyses the impact of the pandemic on the livelihood and the physical, mental and emotional well-being of women domestic workers through a primary survey conducted in Lucknow city in 2020 and 2021. The paper highlights the effect of lockdown on their livelihoods, savings, food security, family life and mental health. It also analyses the effectiveness of the outreach of government support to this vulnerable segment of the urban informal sector. Given the vulnerability of this group to various forms of exploitation, the authors suggest that policy interventions aimed at providing welfare and social protections will need to be coupled with strong political will and increased social consciousness to have an enduring impact. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

Association Between COVID-19 Pandemic and Serious Mental Illness: Systematic Review within Salutogenesis Model for Public Health Management

Background: The outbreak of the COVID-19 pandemic, the constant transformation of the SARS-COV-2 virus form, exposure to substantial psychosocial stress, environmental change, and isolation have led to the inference that the overall population's mental health could be affected, resulting in an increase in cases of psychosis. Objective(s): We initiated a systematic review to determine the impact of the SARS-COV-2 virus and its long-term effects-in both symptomatic and asymptomatic cases-on people with or without psychosis. We envisioned that this would give us an insight into effective clinical intervention methods for patients with psychosis during and after the pandemic. Method(s): We selected fifteen papers that met our inclusion criteria, i.e., those that considered participants with or without psychiatric illness and exposed to SARS-COV-2 infection, for this review and were retrieved via Google, Google Scholar, MEDLINE, PubMed, and PsychINFO Database. Key Gap: There is a dearth of research in understanding how COVID-19 affects people with or without a prior personal history of psychosis. Result(s): The systematic review summary provides insight into the state of knowledge. Insights from the systematic review have also been reviewed from the salutogenesis model's perspec-tive. There is moderate evidence of new-onset psychosis during the COVID-19 pandemic in which some antipsychotics treated the psychotic symptoms of patients while treating for COVID-19. Suggestions and recommendations are made for preventive and promotive public health strategies. Conclusion(s): The Salutogenesis model and Positive Psychology Interventions (PPI) provide another preventive and promotive public health management approach.Copyright © 2023 Bentham Science Publishers.

A 360° view of the inflammasome: Mechanisms of activation, cell death, and diseases.

Inflammasomes are critical sentinels of the innate immune system that respond to threats to the host through recognition of distinct molecules, known as pathogen- or damage-associated molecular patterns (PAMPs/DAMPs), or disruptions of cellular homeostasis, referred to as homeostasis-altering molecular processes (HAMPs) or effector-triggered immunity (ETI). Several distinct proteins nucleate inflammasomes, including NLRP1, CARD8, NLRP3, NLRP6, NLRC4/NAIP, AIM2, pyrin, and caspases-4/-5/-11. This diverse array of sensors strengthens the inflammasome response through redundancy and plasticity. Here, we present an overview of these pathways, outlining the mechanisms of inflammasome formation, subcellular regulation, and pyroptosis, and discuss the wide-reaching effects of inflammasomes in human disease.

The impact of Covid-19 pandemic on public transit use: Case study of Konya city

Since March 2019, Turkey has been enforcing various measures on the policies based on the trends in the COVID-19 cases. To restrain the spread of virus, policies limiting the mobility of people (i.e. lock downs, remote working and travel bans) were applied as in many other countries. Furthermore, social distancing calls for health concern directly caused a major reduction in public transit (PT) use. However, economic activities and new normal conditions required return of a part of the commute travels, which brought the issue of use of PT modes. This study focuses on the comparison of the PT mobility during the month of April in the 2019 (pre-pandemic), in 2020 during restrictions and in 2021 under new normal condition using the Smart Card (SC) data in Konya, Turkey. Monthly, daily and hourly distribution of ridership patterns are compared as well as usage patterns and characteristics of different bus lines are examined in detail. The results suggested that during the restrictions, the ridership was about one eight of the pre-pandemic periods, while it increased to 2.5 million ridership in 2021 which is still very low. Daily ridership in 2020 showed no PT mobility due to lockdowns, while during weekdays, hourly ridership distributions were changed parallel to changes in the work/education activity schedules. Evaluation of the bus lines having highest ridership in 2019 with 2020 and 2021 showed some of the bus lines were cancelled during the pandemic and routes/frequencies changed. The results showed the importance of PT management during pandemic which is very challenging due to economic loss and fear of infection by public. However, it should be emphasized the importance of continuation of public transportation in terms of accessibility and equity for all. © 2023 The Authors. Published by ELSEVIER B.V.

Blockchain-based EHR System for Indian Healthcare Industry using Aadhar

In today's world, digital technologies are advancing at a rapid pace. Almost every industry has benefited from this ongoing change. In the health sector, the digitization of medical records was proposed decades ago. Whereas some developed countries have successfully adopted and implemented Electronic Health Record (EHR) systems. Developing countries like India still heavily rely on paper-based medical records. Although there are a number of systems for electronic medical record management, they have issues related to interoperability, timely access, and storage. Due to poor infrastructure and design, the current systems are not robust for communicating and tracking medical records. The need for a better EHR system was highly emphasized during the COVID-19 pandemic. The two major shortcomings of the existing system are a lack of interoperability, which causes delays in sharing the information, and a lack of standardization, due to which the data quality of the data that is shared suffers. To mitigate these issues, we need a nationwide EHR system. Another issue is the lack of a ubiquitous UPI (Unique Patient Identifier). In a country like India, the second most populated country in the world, Aadhar is the best option for UPI, which can be used for creating a national EHR system. In this paper, we have presented a framework for a standardized, interoperable, and unified EHR system based on blockchain technology with Aadhar as the UPI. Using blockchain as the base of this model provides numerous advantages over a cloud-based system, like decentralization, better security, immutability, and traceability. © 2023 IEEE.

The social value of a SARS-CoV-2 vaccine: Willingness to pay estimates from four western countries.

SARS-CoV-2 vaccines give rise to positive externalities on population health, society and the economy in addition to protecting the health of vaccinated individuals. Hence, the social value of such a vaccine exceeds its market value. This paper estimates the willingness to pay (WTP) for a hypothetical SARS-CoV-2 vaccine (or shadow prices), in four countries, namely the United States (US), the United Kingdom, Spain and Italy during the first wave of the pandemic when COVID-19 vaccines were in development but not yet approved. WTP estimates are elicited using a payment card method to avoid "yea saying" biases, and we study the effect of protest responses, sample selection bias, as well as the influence of trust in government and risk exposure when estimating the WTP. Our estimates suggest evidence of an average value of a hypothetical vaccine of 100-200 US dollars once adjusted for purchasing power parity. Estimates are robust to a number of checks.

Artificial Bee Colony for Logic Mining in Credit Scoring

During the SARS-CoV-2 (Covid-19) pandemic, credit applications skyrocketed unimaginably. Thus, creditors or financial entities were burdened with information overload to ensure they provided the proper credit to the right person. The existing methods employed by financial entities were prone to overfitting and did not provide any information regarding the behavior of the creditor. However, the outcome did not consider the attribute of the creditor that led to the default outcome. In this paper, a swarm intelligence-based algorithm named Artificial Bee Colony has been implemented to optimize the learning phase of the Hopfield Neural Network with 2 Satisfiability-based Reverse Analysis Methods. The proposed hybrid model will be used to extract logical information in the credit data with more than 80% accuracy compared to the existing method. The effectiveness of the proposed hybrid model was evaluated and showed superior results compared to other models.

Exploring year-to-year changes in station-based bike sharing commuter behaviors with smart card data

Station-based bike sharing (SBBS) not only provides commuters with direct "door-to-door" trips, but also plays a vital role in addressing the "first/last mile" challenges for public transportation system. However, there is a lack of research into portraying year-to-year changes in SBBS commuter behaviors. With five-year (from 2016 to 2020) SBBS smart card data collected in Nanjing, China, a longitudinal analysis is performed in this study to trace yearly dynamics of commuter behaviors at an individual level. We identify two sorts of SBBS commuters (i.e., SBBS-alone and SBBS-metro commuters) based on users' spatial-temporal travel regularities. The paper finds that (i) the number of SBBS users presented a considerable fluctuation trend over a five-year span, while the proportion of SBBS commuters stabilized at an equilibrium level;(ii) the COVID-19 outbreak accelerated the decline in the proportion of female and young SBBS commuters;(iii) most SBBS commuters were recorded for only one year out of five, while the share of commuters who used SBBS for four years or more is tiny, < 5%;(iv) the trip duration of SBBS-alone commuters was significantly longer than that of SBBS-metro commuters, and both showed some increase during the COVID-19 pandemic;(v) the number of non-loop trip chains was dramatically higher than that of loop trip chains, which is more prominent among SBBS-metro commuters. Our findings could provide valuable insights into the behavioral dynamics of SBBS commuters and offer recommendations on how policy makers and transportation planners could respond to these precipitate changes. (PsycInfo Database Record (c) 2023 APA, all rights reserved)

Using IoT-Enabled RFID Smart Cards in an Indoor People-Movement Tracking System with Risk Assessment

As the COVID-19 pandemic ravaged the planet at a standstill, remote employment seemed inescapable. Still, for some businesses that rely on the on-site presence of employees, this was a lethal blow. As time passed, restrictions got looser and allowed people to strike a balance between on-site and remote work. Thus, tracking people's indoor movements for purposes involving activity inference, security, and contact tracing is more crucial than ever before. This research explores the applicability of (Radio Frequency Identification) RFID contactless smart cards in tracking people's movement within an enclosed establishment by building a proof-of-concept prototype that allows the mentioned purposes. Furthermore, the system underwent multiple test phases to verify that the system meets the functional and non-functional requirements listed to ensure the system's operational success. Consequently, the test results prove that: 1) the system is behaving as intended;2) the system is secure from known high-risk vulnerabilities;and 3) the system satisfies user requirements and standards, thus fulfilling the functional and non-functional requirements for a human-tracking movement system. © 2023 by the authors.

The SMeFT Decks: A Card-Based Ideation Tool for Designing Hybrid Digital Boardgames for Distanced Play

Increasing numbers of people have turned to playing boardgames with physically distant friends and family via technological tools, particularly since the start of the COVID-19 pandemic. However, commercial hybrid digital boardgames (HDBs) are designed for co-located, rather than distanced, play and there is a need for more specific tools to support hybrid game design. This paper introduces the SMeFT Decks, a set of card decks to aid in the design of HDBs for distanced play, which support Story, Mechanism, Function and Technology. We describe the use of these cards for design ideation and demonstrate four game concepts for distanced play stemming from the use of these decks in participatory workshops. We report evaluative feedback from a pilot study and from 46 participants who used these cards across nine design workshops and reflect on what we learned from observing this process. Results suggest that the SMeFT Decks are a productive ideation tool for aiding in the design of HDBs for distanced play in collaborative workshop settings. © 2022 ACM.

Managing Events: The IoT Way

The concept of IoT in the current world where speed, accuracy and efficiency are of a high importance, can do wonders if implemented in a structured manner, into a machine, project, hardware, idea which can improve technology. So, IoT has its application in the Events. Events can be of many types and there is a need of man power to handle the events efficiently. People gather in huge numbers if there is a political event, whereas there is limited audience in a cultural show or less people in a marriage function. Any of such events, if handled smartly, can ease the tasks of humans, as well as provide speed and accuracy and ensure proper event management and organization. This project demonstrates a hardware for the entry-exit of people for any event, through the technology of Radio Frequency Identification (RFID), Wireless Fidelity (Wi-Fi), and main heart as ESP 8266 Controller. The software simulation in Cisco Packet Tracer shows a general event organization related to a hotel or government-based area, where different sections are integrated to control and handle the event in a smart way. The use of RFID indicates the contactless operation for monitoring the attendee entry-exit, due to the current COVID-19 protocols. So, such systems are safe and smart to execute. © 2022 IEEE.

On the interdependencies between mortgage, credit card and auto loans delinquency rates: evidence from the US states plus the District of Columbia

PurposeThis study aims to explain how delinquency shocks in one type of debt contaminate the others. That is, the authors aim to shed light on the time pattern of delinquencies in different debt types.Design/methodology/approachThis study analyzes the interdependencies between mortgage, credit card and auto loans delinquency rates in the USA from 2003 to 2019, using a panel VAR-X, the panel Granger causality tests and the Geweke linear dependence measures. The authors also compute the impulse response functions of a shock to one kind of debt on the others and decompose the variance of the forecast errors.FindingsThe authors find a statistically significant bidirectional Granger causality between the delinquencies. The Geweke measures of linear dependence and the Dumitrescu and Hurlin Granger non-causality tests support that mortgage predominantly causes credit card and auto loan delinquencies. Auto loans also cause credit card delinquencies. The impulse response functions confirm this pattern. This scenario aligns with a sequence where debtors consider rational first to default on credit cards, second on auto loans and only on mortgages in the last instance. Indeed, credit card delinquencies Granger-cause delinquencies in other debts when it occurs.Originality/valueTo the best of the authors' knowledge, this is the first study to focus on the temporal pattern of delinquency rates for all the US states, using panel data. Furthermore, the results call for policymakers to design regulations to break the transmission channel from debt delinquencies.

The COVID-19 shock and consumer credit: Evidence from credit card data.

We study the dynamic effect of the COVID-19 shock on credit card use in 2020. Local case incidence had a strong negative effect on credit card spending in the early months of the pandemic, which diminished over time. This time-varying pattern was driven by the fear of the virus, rather than government support programs, consistent with the "pandemic fatigue" of consumers. Local pandemic severity also had a strong effect on credit card repayments. These spending and repayment effects offset each other, resulting in no effect on credit card borrowing, consistent with credit-smoothing behavior. The local stringency of nonpharmaceutical interventions also had a negative effect on spending and repayments, albeit smaller in magnitude. We conclude that the pandemic itself was a more important driver of changes in credit card use than the public health policy response.

Credit card behaviors in young adults: Influences of parents' unintentional financial behaviors

Today's young adults are faced with challenging economic times ranging from extreme debt to the COVID-19 pandemic. They have also been raised with powerful technology, which has created an instant gratification world. This instant gratification may cause irrational thoughts to obtain products and services immediately despite the high costs of credit. To help understand the family's role in a person's financial wellbeing, Family Financial Socialization Theory (FFST) has shown that parents are influential on young adults' credit card demographics, but little is known about actual behaviors. Thus, how do young adults manage their credit cards behaviors based on what they observed from their parents?This research focuses on the relationship of family influences on young adult credit card behaviors, specifically parents' unintentional financial behaviors. Also, this analysis uses financial attitudes and knowledge as a mediator to that relationship. This study employed structural equation modeling (SEM) to analyze the relationships among the latent factors and measured variables and test the associations hypothesized in the research model. The sample consisted of 850 responses from a panel of young adults ages 18-26, living in the United States. This study found that, while there is not a direct relationship between family influences and credit card behaviors, a young adult's financial attitudes and knowledge weakly mediates an indirect relationship between them. (PsycInfo Database Record (c) 2023 APA, all rights reserved)

Post-pandemic Economic Transformations in the United States of America

The COVID-19 pandemic has impacted economic activity not only in the United States, but across the globe. Lockdown and travel restrictions imposed by local authorities have led to change in customer preferences and thus transformation of economic activity from traditional areas to new regions. While most changes have been temporary and short term, some of them have been observed to be of permanent nature. Using large-scale aggregated and anonymized transaction data across various socio-economic groups, we analyse and discuss such temporary relocation of citizens' economic activities in metropolitan areas of 15 states in the US. The results of this study have extensive implications for urban planners and business owners, and can provide insights into the temporary relocation of economic activities resulting from an extreme exogenous shock like the COVID-19 pandemic. © 2022 IEEE.

Synthetic ID Card Image Generation for Improving Presentation Attack Detection

Currently, it is ever more common to access online services for activities which formerly required physical attendance. From banking operations to visa applications, a significant number of processes have been digitised, especially since the advent of the COVID-19 pandemic, requiring remote biometric authentication of the user. On the downside, some subjects intend to interfere with the normal operation of remote systems for personal profit by using fake identity documents, such as passports and ID cards. Deep learning solutions to detect such frauds have been presented in the literature. However, due to privacy concerns and the sensitive nature of personal identity documents, developing a dataset with the necessary number of examples for training deep neural networks is challenging. This work explores three methods for synthetically generating ID card images to increase the amount of data while training fraud-detection networks. These methods include computer vision algorithms and Generative Adversarial Networks. Our results indicate that databases can be supplemented with synthetic images without any loss in performance for the print/scan Presentation Attack Instrument Species (PAIS) and a loss in performance of 1% for the screen capture PAIS. Author

Credit Card Fraud Detection using Machine Learning Techniques

The COVID-19 pandemic has caused a huge decline in money usage, with everything turning online these days. It has contributed to an increase in contactless payments that was unimaginable before. A credit card is the most extensively used method of payment, and it is becoming increasingly digital as the number of daily electronic transactions increases, making it more vulnerable to fraud. Credit card firms have suffered losses because of widespread card fraud. The most common worry is the recognition of credit card fraud. As a result, organizations are looking toward advanced device understanding technologies since they can handle a lot of data and spot irregularities that humans would miss. The development of effective To stop these losses, fraud detection algorithms are essential. An increasing number of these algorithms rely on cutting-edge computer methods that can assist fraud investigators. However, the appearance of the full-proof Fraud Detection System demands the use of high performing algorithms that are both exact and sturdy enough to handle massive amounts of data. The algorithm is run using open-source software using R statistical programming. This project tries to provide options by studying several fraud detection systems and highlighting their strengths and limitations. © 2022 IEEE.

Spend as you were told: Evidence from labeled COVID-19 stimulus payments in South Korea.

We test the income fungibility assumption from standard economic theory by analyzing spending responses to South Korea's labeled COVID-19 stimulus payments. We exploit unique policy rules for identification: (1) recipients cannot use payments outside their province of residence, and (2) they can only use payments at establishments in pre-specified sectors. Using data on card transactions in Seoul, we find that households do not consider stimulus payments fungible. Compared to Seoul residents' benchmark spending responses to cash income gains by sector, the stimulus payments disproportionately increased Seoul residents' spending in the allowed sector compared to the non-allowed sector. The payments did not increase non-Seoul residents' card spending. Our results imply that labeled stimulus payments with usage restrictions can boost household consumption spending in targeted sectors or locations during economic recessions.

Performance of Existing and Novel Symptom- and Antigen Testing-Based COVID-19 Case Definitions in a Community Setting.

Point-of-care antigen tests are an important tool for SARS-CoV-2 detection yet are less clinically sensitive than real-time reverse-transcription PCR (RT-PCR), impacting their efficacy as screening procedures. Our goal in this analysis was to see whether we could improve this sensitivity by considering antigen test results in combination with other relevant information, namely exposure status and reported symptoms. In November of 2020, we collected 3,419 paired upper respiratory specimens tested by RT-PCR and the Abbott BinaxNOW antigen test at two community testing sites in Pima County, Arizona. We used symptom, exposure, and antigen testing data to evaluate the sensitivity and specificity of various symptom definitions in predicting RT-PCR positivity. Our analysis yielded 6 novel multi-symptom case definitions with and without antigen test results, the best of which overall achieved a Youden's J index of 0.66, as compared with 0.53 for antigen testing alone. Using a random forest as a guide, we show that this definition, along with our others, does not lose the ability to generalize well to new data despite achieving optimal performance in our sample. Our methodology is broadly applicable, and our code is publicly available to aid public health practitioners in developing or fine-tuning their own case definitions.