Can Destination Cards Help to Shape Areas' Sustainability?

This paper examines whether destination cards can simultaneously serve tourists' needs and sustainability goals. It provides useful insights for tourism authorities and policymakers in designing a smart tourist card that meets the needs of tourists while preserving and supporting areas' wellbeing. Taking Thessaloniki city as a case study, a tourist survey, designed based on the key features of European destination cards, was implemented to identify needs and motivations. Interesting insight was revealed: tourists want to self-explore the city, are coming with their families, are history-lovers and gastronomy-keen, and are strongly willing to be provided with a destination card offering unlimited access to public transport. The latter reveals an opportunity for the city;the tourists are willing to use sustainable mobility options, which means that a base of sustainable travelling exists. The proposed Thessaloniki smart card can bring together tourists' needs with the city's sustainability goals;the development of tourist packages, including sustainable mobility provisions, walking-talking tours, and bike rentals, should be the backbone of the card. The next challenge for the city is to build a cooperation network to support this smart destination card implementation and promotion.

An unbiased regression type estimator of proportion in randomized response sampling by using analysis of variance mechanism

In this article, two new estimators of population proportion of a sensitive characteristic are introduced by using a method analogous to Analysis of Variance (ANOVA). Then, a new unbiased regression type estimator is developed by utilizing these two estimators. The proposed estimator is, then, compared with its competitor at the same level of protection of the respondents. Also included is a study, based on data collected during summer 2021, of the currently hot topic of estimating the proportion of students, 18 years and older, returning to schools in fall 2021, who tested positive for COVID-19.

Privacy-Preserving Fast Three-Factor Authentication and Key Agreement for IoT-Based E-Health Systems

Electronic healthcare (e-health) systems have received renewed interest, particularly in the current COVID-19 pandemic (e.g., lockdowns and changes in hospital policies due to the pandemic). However, ensuring security of both data-at-rest and data-in-transit remains challenging to achieve, particularly since data is collected and sent from less insecure devices (e.g., patients' wearable or home devices). While there have been a number of authentication schemes, such as those based on three-factor authentication, to provide authentication and privacy protection, a number of limitations associated with these schemes remain (e.g., (in)security or computationally expensive). In this study, we present a privacy-preserving three-factor authenticated key agreement scheme that is sufficiently lightweight for resource-constrained e-health systems. The proposed scheme enables both mutual authentication and session key negotiation in addition to privacy protection, with minimal computational cost. The security of the proposed scheme is demonstrated in the Real-or-Random model. Experiments using Raspberry Pi show that the proposed scheme achieves reduced computational cost (of up to 89.9% in comparison to three other related schemes).

Mechanical Ventilation in COVID

From the start of the pandemic, amid the frequency of cases with COVID-19 associated respiratory failure, mechanical ventilation has been the object of controversy. Reports associating its use with higher mortality, likely reflecting the severity of an unknown illness devastating the entire world, as well as the turmoil caused by the lack of sufficient equipment to supply the increasing demands in our hospitals, both were points of attention for media and public in general. However, from the clinical perspective, the need to apply different methods or to deviate from stablished guidelines to be able to adequately support these patients, was soon noticed. Multiple publications were guiding clinicians in the obscured territory of the unknown disease and to its variable impact on the respiratory system. This chapter aims to summarize the knowledge acquired throughout the pandemic, describing some of the elements of COVID-19 respiratory failure as well as its management with mechanical ventilation. The chapter recovers some of the increasing information appearing almost daily in the literature. We recognize that given the changing nature of the disease and the progressive knowledge of the same, some of the concepts covered in this chapter might be subject of some review or modification at the moment of the publication. We, the authors, have attempted to summarize the existing evidence and to maintain a basic conceptual approach to the management of COVID-19 respiratory failure. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.

Playing the wild cards: Antecedents of family firm resilience

The purpose of this study is to explore how family firms respond to wild cards. We aim to capture the under-standing of family firm owners/managers of what wild cards are in terms of frequency, kind, and impact. We also examine how familiness and entrepreneurial orientation form the resilience and survival of family firms when facing wild cards. The scope of our attention is limited to extreme events so far overlooked in the family firm resilience literature, and the empirical context of our study involves the COVID-19 pandemic. Our findings show that the response to wild cards depends on the understanding of those extreme situations that family firms managers/owners develop. Deep time horizon is relevant in developing a useful understanding of wild cards, and generational involvement helps to socially construct it. After developing an understanding, family firm man-agers/owners use decision making preferences in selecting their response to wild cards. Our study offers a behavioral take on family firms resilience, and provides a fine grained view incorporating behavioral constructs.

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.

Critical success factors of smart card technology in South African public hospitals

Background: Even though the government has set several admirable targets for raising the standard of healthcare, as highlighted by communities and media reports, public health institutions' services continue to fall short of patients' expectations and basic standards of care. For this reason, the general public has lost faith in the healthcare system. The public healthcare system in South Africa is completely dysfunctional and urgently needs to be transformed to serve the majority of those who use public hospitals. Objectives: The study aimed to improve healthcare for the majority of South Africans by investigating the critical success factors (CSFs) that influence the adoption of smart card technology (SCT) in South African public hospitals. Methods: A thorough review of peer-reviewed literature was conducted to determine potential barriers to adopting SCT. Furthermore, a hybrid model that combines the Health Unified Technology of Acceptance Theory (HUTAUT) model, DeLone and McLean IS success model (D&M) and the diffusion of innovation (DOI) theory will be developed, validated and tested to identify the CSFs adoption of SCT in public hospitals in South Africa. Results: The validated research model has been developed to be adopted by nurses at public hospitals. Conclusion: This research will contribute to the development of a new framework that identifies the CSFs for SCT adoption in South African public hospitals. Contribution: The study's results will make a special contribution to the body of knowledge in the fields of health informatics, particularly e-health.

Permanent layoff and consumer credit card loss forecasting

PurposeThe unemployment rate (UR) is the leading macroeconomic indicator used in the credit card loss forecasting. COVID-19 pandemic has caused an unprecedented level of volatility in the labor market variables, leading to new challenges to use UR in the credit risk modeling framework. This paper examines the dynamic relationship between the credit card charge-off rate and the unemployment rate over time.Design/methodology/approachThis study uses quarterly observations of charge-off rates on credit card loans of all commercial banks from Q1 1990 to Q4 2020. Univariate, multivariable, machine learning, and regime-switching time series modeling are employed in this research.FindingsThe authors decompose UR into two components – temporary and permanent UR. The authors find the spike in UR during COVID-19 is mainly attributed to the surge in temporary layoffs. More importantly, the authors find that the credit card charge-off rate is primarily driven by permanent UR while temporary UR has little predictive power. During recessions, permanent UR seems to be a stronger indicator than total UR. This research highlights the importance of using permanent UR for credit risk modeling.Originality/valueThe findings in the research can be applied to the credit card loss forecasting and CECL reserve models. In addition, this research also has implications for banks, macroeconomic data vendors, regulators, and policymakers.

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.

An Analysis of COVID-19's Effects on Online Payments Using ANOVA

Adoption of digital payment methods rises during the COVID-19 pandemic. The coronavirus outbreak is affecting how consumers make payments. The majority of customers want to continue using digital payments once the virus has been contained. Global consumers were using them more frequently than they had before the epidemic. The main payment options gaining from this shift are e-Wallets and contactless cards as people use less cash and make more online purchases. Prior to COVID 19, fewer people used digital payment methods worldwide. When Covid-19 spread and physical transactions were on the verge of collapse, digital payments became a reality. This study's primary goals are to evaluate the effectiveness ofonline payment apps used by respondents during Covid 19 and consumer perceptions of the uptake of these methods. Both primary and secondary methods are applied in the process. Structured questionnaires were given to the residents of the Chennai area using the primary data approach. Articles, journals, and various forms of Internet have been utilized in secondary data. The collected data is analysed through Analysis of Variance method. The conclusion of this study shows that people were satisfied that online payment app is more convenient, time saving and easy to adopt. Thoughthere are many barriers in online payment app there were some preventive measures and security. © 2022 IEEE.

Covid-19 Vaccine safety and adverse event analysis from Pakistan

Covid immunization commenced on 2nd Feb 2021 in Pakistan and as of 7th Sep 2021, over 84 million vaccine doses were administered in Pakistan, of which 72% procured by the government, 22% received through Covax and 6% were donated. The vaccines rolled out nationally included: Sinopharm, Sinovac and CanSinoBIO (China), AstraZeneca (UK), Moderna and Pfizer (USA), Sputnik (Russia), and PakVac (China/Pakistan). About half of the eligible population in Pakistan (63 m) had received at least one dose of Covid vaccine as of Sep 2021. Pakistan National Pharmacovigilance Centre (PNPC) in coordination with WHO, MHRA and Uppsala Monitoring Centre (UMC) established pharmacovigilance centers across Pakistan. The Covid vaccine AEFIs in Pakistan were mainly reported via NIMS (National Immunization Management System), COVIM (Covid-19 Vaccine Inventory Management System), 1166 freephone helpline and MedSafety. There have been 39,291 ADRs reported as of 30th Sept 2021, where most reported after the first dose (n = 27,108) and within 24-72 h of immunization (n = 27,591). Fever or shivering accounted for most AEFI (35%) followed by injection-site pain or redness (28%), headache (26%), nausea/vomiting (4%), and diarrhoea (3%). 24 serious AEFIs were also reported and investigated in detail by the National AEFI review committee. The rate of AEFIs reports ranged from 0.27 to 0.79 per 1000 for various Covid vaccines in Pakistan that was significantly lower than the rates in UK (~4 per 1000), primarily atrributed to underreporting of cases in Pakistan. Finally, Covid vaccines were well tolerated and no significant cause for concern was flagged up in Pakistan's Covid vaccine surveillance system concluding overall benefits outweighed risks.Copyright © 2022

Reexamine the Role of the Financial Institution Management During Card Debt Crisis or the COVID-19

Due to the increasing popularity of financial technology and the lifting of financial regulations, various financial institutions have become increasingly competitive and actively expand their consumer finance business. Changes in generational consumption behavior have led to excessive credit expansion, excessive debt or bad credit records. All of these result in the emergence of adverse selection and moral hazard problems of information asymmetry, and finally cause the card debt crisis in 2005. This article focuses on variables such as the number of cards in circulation, retail sales volume, revolving balance, and overdue ratios of credit cards in public and private banks, and examine whether the information asymmetry in the credit card market has been improved ,with the financial institution management. Furthermore, due to the COVID-19 exploring whether the information asymmetry has been worsened or improved deserves the attention of the financial authority again. The results reveal that continuous financial institution management is very important and effective during the card debt period or the pandemic.

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.

A Novel Approach to Combat COVID-19 using Smart-ID-Cards

COVID-19, A Pandemic with its increasing pace has spread across the globe. The medical care system was badly hit by it as the number of patients fared the number of available hospital beds and other facilities required to treat patients. To rescue, various Internet of Things (IoT) based devices were proposed to combat COVID-19 by offering a helping hand to the medical care system. The pace at which the death rate was increasing, it became the need to combat the root cause of COVID-19, the root cause being the quick spread. ID-Card though not so famous IoMT (Internet of Medical Things) device can be made to work smart, smart enough to monitor the home isolated patients, to keep a check on a precautionary distance measure and much more. The study aims to explore and discuss the state-of-the-art of various IoT to control the novel Coronavirus (COVID-19) spread by tracing out positive patients and stopping this chain by tracing symptoms just a click away. The IoMT Smart-ID-Card is proposed to easefully detect, monitor, and combat COVID-19. © 2022 IEEE.

A network-based analysis to assess COVID-19 disruptions in the Bogotá BRT system

The global COVID-19 crisis has severely affected mass transit in the cities of the global south. Fear of widespread propagation in public spaces and the dramatic decrease in human mobility due to lockdowns have resulted in a significant reduction of public transport options. We analyze the case of TransMilenio in Bogotá, a massive Bus Rapid Transit system that is the main mode of transport for an urban area of roughly 10 million inhabitants. Concerns over social distancing and new health regulations reduced the number of trips to under 20% of its historical values during extended periods of time during the lockdowns. This has sparked a renewed interest in developing innovative data-driven responses to COVID-19 resulting in large corpora of TransMilenio data being made available to the public. In this paper we use a database updated daily with individual passenger card swipe validation microdata including entry time, entry station, and a hash of the card's ID. The opportunity of having daily detailed minute-to-minute ridership information and the challenge of extracting useful insights from the massive amount of raw data (∼1,000,000 daily records) require the development of tailored data analysis approaches. Our objective is to use the natural representation of urban mobility offered by networks to make pairwise quantitative similarity measurements between daily commuting patterns and then use clustering techniques to reveal behavioral disruptions as well as the most affected geographical areas due to the different pandemic stages. This method proved to be efficient for the analysis of large amount of data and may be used in the future to make temporal analysis of similarly large datasets in urban contexts. © The Author(s) 2023.

Design and Build Body Temperature Detection and Personal Identity Recognition Through E-ID Card and Photo Based on IoT

One of the efforts to enforce health protocols is the use of body temperature checkers in every public place. Just like on campus, body temperature is checked using a thermometer gun. Problems encountered when checking the health protocol system included temperature data collection and the identity of visitors who entered the campus. Therefore, so that temperature control can be done automatically and visitor history can be viewed and saved automatically every day, body temperature detection and personal identity recognition through E-ID card and photo based on IoT is made. The realization of temperature checks can use the MLX90614 sensor which has the advantage of being able to read body temperature without requiring direct contact between the body and the sensor and is integrated using RFID which uses E-ID as an identity tag and ESP32CAM to take pictures of visitors' faces to be recorded and sent data to the internet. The purpose of this research is to design a body temperature detector and identify self-identity through IoT-based E-ID and Photos and explain the work system and performance of body temperature detectors. From the results of testing for body temperature detection and self-identification through IoT-based E-ID and photos, the results show that this system is able to retrieve temperature data, E-ID, and facial photos. The standard error that occurs during this measurement is 0.03 and the temperature difference between the two tools is 0.18° C. © 2022 IEEE.

Credit Card Fraud Detection: Challenges and Techniques

Credit card usage has risen dramatically as a result of rapid advancements in electronic commerce and the unexpected circumstance of COVID. With credit cards becoming the most popular payment method for both offline and online transactions, the number of cases of fraud associated with them is rapidly increasing. In case of online fraud, it is not necessary for the perpetrators to be present at the scene of crime. The fraudulent activities can be accomplished by them in the seclusion of their homes through a multitude of methods for disguising their identities. VPNs are one way to obscure one's identity, as is routing communication through any Tor network for the victim, making it difficult to track back the culprit. © 2022 IEEE.

Perspectives and parameters for the use of digital banking

For fear of Covid-19 infection, the population opted for the use of technological means to make payments by electronic means deciding to use other means of payment (credit or debit card) to perform any type of banking service, hence the banks offer cards with different benefits, the objective is to measure the perspectives and parameters requested by users to obtain a credit or debit card, with a descriptive, quantitative, cross-sectional, correlational and predictive study, with a sample of 1646 surveys conducted online and anonymously, using the measurement instrument with questions about digital banking (pre and post pandemic) whose parameters are digital banking services and motivational factors for its use. The results show that users are increasingly interested in adopting digital band services in a meaningful way. In conclusion the measurement instrument is suitable for application, parameters such as cellular service (App) and account status information are priority for the population. © 2022 IEEE.