Rare Disaster Events, Growth Volatility, and Financial Liberalization: International Evidence

Purpose - This paper elucidates a nexus between the occurrence of rare disaster events and the volatility of economic growth by distinguishing the likelihood of rare events from stochastic volatility. We provide new empirical facts based on a quarterly time series. In particular, we focus on the role of financial liberalization in spreading the economic crisis in developing countries. Design/methodology - We use quarterly data on consumption expenditure (real per capita consump-tion) from 44 countries, including advanced and developing countries, ending in the fourth quarter of 2020. We estimate the likelihood of rare event occurrences and stochastic volatility for countries using the Bayesian Markov chain Monte Carlo (MCMC) method developed by Barro and Jin (2021). We present our estimation results for the relationship between rare disaster events, stochastic volatility, and growth volatility. Findings - We find the global common disaster event, the COVID-19 pandemic, and thirteen country-specific disaster events. Consumption falls by about 7% on average in the first quarter of a disaster and by 4% in the long run. The occurrence of rare disaster events and the volatility of gross domestic product (GDP) growth are positively correlated (4.8%), whereas the rare events and GDP growth rate are negatively correlated (-12.1%). In particular, financial liberalization has played an important role in exacerbating the adverse impact of both rare disasters and financial market instability on growth volatility. Several case studies, including the case of South Korea, provide insights into the cause of major financial crises in small open developing countries, including the Asian currency crisis of 1998. Originality/value - This paper presents new empirical facts on the relationship between the occurrence of rare disaster events (or stochastic volatility) and growth volatility. Increasing data frequency allows for greater accuracy in assessing a country's specific risk. Our findings suggest that financial market and institutional stability can be vital for buffering against rare disaster shocks. It is necessary to preemptively strengthen the foundation for financial stability in developing countries and increase the quality of the information provided to markets.

Comparison of Supervised Learning Models for COVID-19 Confirmed Cases Prediction using Correlation Analysis

The COVID-19 virus, which first appeared in 2019, has a strong contagious power and is highly spread by people's mobility. In this study, correlation analysis is used in statistical preprocessing of dataset which further used to predict the COVID-19 confirmed cases for next day. Data is divided into two sets by organizing the data set by data preprocessing using correlation analysis. The first dataset is Google Mobility Data of COVID-19 infection with six variables. The second dataset is Google Mobility Data of COVID-19 infection with two variables: (1) Retail stores and leisure facilities (2) Grocery stores and pharmacies. The results of predicting the number of confirmed cases are compared using four supervised machine learning models. Furthermore, the soft voting method is used to show more improved results than the individual performances of each method. © 2022 IEEE.

Effects of Sociability, Service to Nursing Students on Nursing Professionalism in the Covid-19 Pandemic

This study was conducted to identify factors affecting professionalism of nursing students. This data is meant to be used as a starting point for identifying the educational methods required to build nursing professionalism. From March 18 through April 8, 2022, the subjects of this study were surveyed online through Google. With this, 194 questionnaires were collected from fourth-year students of the nursing department at a university. The analysis revealed a positive correlation between sociability, service, and nursing professionalism, with service having the greatest impact on nursing professionalism (p˂.001). Therefore, it is urgent to prepare a non-regular curriculum program that can cultivate the service of nursing students. Furthermore, it is important to create an educational program that may help nursing students become professionals in the field through a variety of volunteer activities, such as non-face-to-face programs, encouraging voluntary participation, and continuous management © 2022,Journal for ReAttach Therapy and Developmental Diversities. All Rights Reserved.

Digital Transformation and Divide of Cities through the COVID-19 Pandemic

The COVID-19 pandemic has affected almost all sectors of society in a short period. In this paper, we study the impact of the COVID-19 pandemic on smart cities through analyses of 311 data of cities and the residents in the United States. We have compared various aspects of municipal governments' service platforms and citizens' requests during pre-COVID, the lockdown, and the rest of the pandemic duration. Among multiple observations from the data, we discover the noticeable changes in the digital transformation of citizens' voices during the COVID-19 pandemic. We observe disparities in service adaptation across many cities, where only a few cities have quickly added pandemic responsive service types and favorable 311 mobile apps in addition to phone and online web services. Besides the digital transformation of residents and municipal governments, we also find that various aspects of divides of residents, such as economic, COVID-related health, and demands are closely related to each other. We have built a comprehensive website that dynamically collects 311 data from municipal open data of cities in the United States that other researchers or urban planners can use to understand citizens' voices better and draw insights. © 2022 IEEE.

Apple In My Eyes (AIME): Liveness Detection for Mobile Security Using Corneal Specular Reflections

The demand for contactless biometric authentication has significantly increased during the COVID-19 pandemic and beyond to prevent the spread of Coronavirus. The global pandemic unexpectedly affords a greater opportunity for contactless authentication, but iris and facial recognition biometrics have many usability, security, and privacy challenges, including mask-wearing and Presentation Attacks (PA). Mainly, liveness detection against spoofing is notably a challenging task as various biometric authentication methods cannot efficiently assess the real user’s physical presence in unsupervised environments. Although several face anti-spoofing methods have been proposed using add-on sensors, dynamic facial texture features, and 3D mapping, most of them require expensive sensors and substantial computational resources, or fail to detect sophisticated 3D face spoofing. This paper presents a software-based facial liveness detection method named “Apple in My Eyes (AIME).”AIME is intended to detect the liveness against spoofing for mobile device security using challenge-response testing. AIME generates various screen patterns as authentication challenges, then passively detects corneal-specular reflection responses from human eyes using a frontal camera and analyzes the detected reflections using lightweightMachine Learning techniques. AIME system components include Challenge and Pattern Detection, Feature Extraction and Classification, and Data Augmentation and Training. We have implemented AIME as a cross-platform application compatible with Android, iOS, and the web. Our comprehensive experimental results reveal that AIME detects liveness with high accuracy at around 200 ms against different types of sophisticated PAs. AIME can also efficiently detect liveness in multiple contactless biometric authentications without any costly extra sensors nor involving users’active responses. IEEE

LIBKDV: A Versatile Kernel Density Visualization Library for Geospatial Analytics

Kernel density visualization (KDV) has been widely used in many geospatial analysis tasks, including traffic accident hotspot detection, crime hotspot detection, and disease outbreak detection. Although KDV can be supported by many scientific, geographical, and visualization software tools, none of these tools can support high-resolution KDV with large-scale datasets. Therefore, we develop the first versatile programming library, called LIBKDV, based on the set of our complexity-optimized algorithms. Given the high efficiency of these algorithms, LIBKDV not only accelerates the KDV computation but also enriches KDV-based geospatial analytics, including bandwidth-tuning analysis and spatiotemporal analysis, which cannot be natively and feasibly supported by existing software tools. In this demonstration, participants will be invited to use our programming library to explore interesting hotspot patterns on large-scale traffic accident, crime, and COVID-19 datasets. © 2022, VLDB Endowment. All rights reserved.

First contact: A series of zoom-based, virtual on call shifts

Aim: COVID-19 has resulted in reduced exposure to on-call shifts where medical students could increase confidence and proficiency in task prioritisation and decision making. Existing 'simulated on-calls' provide a substitute in a controlled environment, however in person teaching has also been limited by COVID-19. Our virtual on-call sessions use ZOOM to replicate the higher-level learning experiences normally conferred by live simulation. Method: We designed a series of virtual 'on-calls' for medical students. Participants were 'on-call', receiving 'bleeps' which were 'answered' by calling a facilitator via ZOOM. The facilitator would roleplay a scenario and the 'Electronic Patient Record' (EPR) on Google Forms contained patient notes and observations. Students needed to collect information from the facilitator and document a management plan into the EPR. Participants received 'bleeps' of varying complexity, urgency and relevance and were expected to prioritise and triage tasks accordingly. Evaluation was via a pre/post session quiz with separate feedback forms. Results: 23 students from 18 universities participated. Students reported increased confidence in managing on-call scenarios, and average scores improved in the post session quiz. Positive feedback was paid to the variety of scenarios, the EPR system and the feeling of realism elicited from the need to triage and prioritise jobs. Conclusions: Our framework uses readily accessible technology to provide interactive learning experience. Feedback suggested students engaged in higher order learning and thinking, achieving our stated aims. We aim to incorporate technologies such as automation software which will allow for a scalable, free, and accessible virtual on call.

Crowd Safety Sensing (CroSS) for the Post Pandemic Era

Due to its long incubation period, aggressive asymptomatic transmission, and new mutations of the virus, COVID-19 is causing multiple pandemic waves worldwide. Despite recent vaccination, social distancing, and social restriction efforts, false negatives, and dormant positives can make pandemics challenging to restrain. In addition to rapid vaccination, effective contact tracing, mask-wearing, and social distancing are critical for out-break containment and for achieving herd immunity. However, the existing technology solutions, such as contact tracing apps and social-distance sensing, have been met with suspicion due to privacy and accuracy concerns and have not been widely adopted. Without achieving a critical mass of individual users, these personal technologies have been rendered useless. On the other hand, large-scale policy efforts have been complicated, requiring the coordination of federal, state, and local governments and regulation enforcement logistics. However, local communities balance these approaches and are an unrealized, powerful resource to prevent future outbreaks.This paper proposes a novel Crowd Safety Sensing (CroSS) for building a sustainable safe community cluster against COVID-19 and beyond using affordable Internet of Things (IoT) technologies. CroSS monitors social distancing policies to small, focused communities for accommodating efficient technology penetration, greater accuracy, effective practices, and privacy policy assistance. We implemented a social distancing method and integrated it into an edge-based IoT system. The experimental results show that CroSS detects false-positive social distancing cases. © 2021 IEEE.

Optical determination of filter effectiveness in potentially aerosol contaminated classrooms during the COVID-19 pandemic

Increasing COVID-19 infections are reason of concern for all the inside workplaces where physical presence is necessary for collaborating. Classrooms are one of the suspected places, where usually students are closely placed to learn together as in times before the pandemic. To reduce the infection rate in classrooms, an air purifier was designed around a commercial filter which removes 99, 9% of particles with 3μm. A baseline optical study of air purification was carried out to ensure effectiveness of the purifier during operation in closed environment. With conclusive evidence of microscopic images, breathing tests and aerosol penetration test using oil, the filter effectiveness was recorded. Optical values for suspended particle counts are recorded for variations in air flow rates of the air purifier and the gradual change is helping to understand the filter performance. Already around 70% minimum effectiveness of one flattened tissue layer removed from the filter was recorded during the tests, where the functional filter is folded in zigzags and 25 times thicker than a single layer. Furthermore, microscopic images showed solids deposited on the filter fabric and fuzzy spots on the tissue could indicate possible dried aerosol spots. This could be the hint supporting the hypothesis that aerosols can be effectively filtered reducing the virus load thus also risk of super-spreading of potential infection risk to an acceptable level. Beyond this research, and with the same group, measurements were made finding out the degree of reduction in potential aerosols particles in a classroom with a continuously aerosol emitting person. On that basis from this and the other optical studies, it was concluded that the spread of COVID-19 virus can be mitigated through effective air purification systems in classrooms and students can continue learning smoothly during the ongoing pandemic. © 2021, Society for Imaging Science and Technology.

Mosaic: Modeling Safety Index in Crowd by Detecting Face Masks against COVID-19 and beyond

In addition to rapid vaccination, predicting possible trajectories of the COVID-19 pandemic is critical to health-care-related policy decisions and infrastructure planning. Growing evidence shows that face masks and social distancing can considerably reduce the spread of respiratory viruses like COVID-19. However, the current pandemic trajectory predictions take overly simplified policy input rather than actual observations of face masks and social distancing practices in a crowd. Thus, it is crucial to monitor and understand the extent of masking practices and assess the safety level in a scalable manner. This paper proposes a novel face masking detection system for Modeling Safety Index in Crowd (Mosaic), a Machine Learning (ML)-based approach for detecting masking in a crowd by building new dense mode crowd mask datasets. Mosaic detects, counts, and classifies the crowd's masking condition and calculates spatiotemporal Safety Index (SI) values for each community instead of detecting individual masking cases. SI data can be shared or published to calculate the area-based SI maps (as opt-in data) for assisting effective policy decisions and relief plans against COVID-19. The experimental results show that Mosaic detects various conditions and types of masking states and calculates SI values of a crowd effectively. This paper proposes a novel face masking detection system for Modeling Safety Index in Crowd (Mosaic), a Machine Learning (ML)-based approach for detecting masking in a crowd by building new dense mode crowd mask datasets. Mosaic detects, counts, and classifies the crowd's masking condition and calculates spatiotemporal Safety Index (SI) values for each community instead of detecting individual masking cases. SI data can be shared or published to calculate the area-based SI maps (as opt-in data) for assisting effective policy decisions and relief plans against COVID-19. The experimental results show that Mosaic detects various conditions and types of masking states and calculates SI values of a crowd effectively. © 2021 IEEE.

CATS: Crowd-based Alert and Tracing Services for building a Safe Community Cluster against COVID-19

COVID-19 has been causing several pandemic waves worldwide due to its long incubation period and hostile asymptomatic transmission. Society should continue to practice social distancing and masking in public despite aggressive vaccinations until achieving population immunity. However, the existing technology solutions, such as contact tracing apps and social-distancing devices, have been faced with suspicion due to privacy and accuracy concerns and have not been widely adopted. This paper proposes a novel infection management system named Crowd-based Alert and Tracing Services (CATS) to build a safe community cluster. CATS applies social distancing and masking principles to small, focused communities to provide higher privacy protection, efficient penetration of technology, and greater accuracy. We have designed a smart tag for managing social distancing. We also implemented a Machine Learning (ML)-based face mask tracking system to build non-binary Safety Impact Values (SIV). © 2021 IFIP.

Where is the age of digitalization heading? The meaning, characteristics and implications of contemporary digital transformation

Digital transformation is perceived not only as a change in certain technology but also as a large transition that will ultimately change our lives for the better. Industry convergence, the key to digital transformation, entails, for firms, both various opportunities for innovation and the crisis of falling behind. Therefore, from the perspective of firms, it is critical to examine how digital transformation affects their industries and products as well as how they perceive and respond to digital transformation. This is ultimately a matter of how firms survive and maintain sustainable growth in this great upheaval of digital transformation. Based on the understanding of the concept of digital transformation, this study explores how high-growth firms perceive various aspects of digital transformation. The findings show that digital transformation involves a change of firms based on constant innovation, not simply the acceptance of technology, and that there is a large digital divide that depends on the firm size and industry type. Based on the above, this study derives implications in terms of the innovation activities of firms to ensure that digital transformation does not serve as a handicap and barrier for firms. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.

Unmanned Aerial Vehicle Last-Mile Delivery Considering Backhauls

Utilizing unmanned aerial vehicles for delivery service has been drawing attention in the logistics industry. Since commercial unmanned aerial vehicles have fundamental limitations on payloads and battery capacities, hybrid ground vehicle and unmanned aerial vehicle models have been actively investigated as practical solutions. However, these studies have focused on linehaul (delivery) demands, excluding a large number of backhaul (pickup) demands. If we consider both demands at the same time, an empty unmanned aerial vehicle that finished linehaul service can be immediately used to serve a backhaul customer. In this study, we investigate the differences that arise by considering backhauls as an additional element of the routing problem. A mixed integer linear programming model is developed, and a heuristic is constructed to solve large-scale problems. To demonstrate the effectiveness of our model, we compare it to existing models using a real-world example. Our solution is also evaluated based on experiments employing a large number of randomly generated datasets. CCBY

An institutional evaluation of the safety and efficacy of same-day administration of pegfilgrastim in patients receiving chemotherapy for lung cancer

Background: Pegfilgrastim is recommended to be administered 24 hours after myelosuppressive chemotherapy (CTX) as prophylaxis for chemotherapy-induced (febrile) neutropenia (CIN/FN). Recent studies have yielded equivocal data on same day versus next day administration of pegfilgrastim. There has been limited real world evidence addressing lung cancer (LC) patients and the use of same day pegfilgrastim. We evaluated our own institutional data on the safety of same day pegfilgrastim administration in LC patients. Methods: A retrospective chart review was performed by searching electronic health records using ICD-9 and ICD-10 codes corresponding with a lung cancer diagnosis between November 1, 2013 and August 31, 2018 at The University of Arizona Cancer Center (UACC). Patients included in the study were 18 years of age or older, diagnosed with biopsyconfirmed lung cancer, treated at UACC, and receiving chemotherapy and pegfilgrastim on the same day. The outcomes of interest included FN incidence after the first cycle and across all cycles of CTX, CIN grade 3/4 and CTX dose delays or hospitalizations due to CIN/FN after first cycle and across all cycles of CTX. Results: 1,181 patient records were reviewed and 114 patients met the inclusion criteria;87 (76%) patients had non-small cell LC and 27 (24%) patients had small cell LC. The median age was 68 years, 52% of patients had cancer stage of 3 to 4, and 63% of patients had 0-1 ECOG status. The FN risk assessment was mild in 72% of patients. The mean (SD) of baseline absolute neutrophil count was 5.68 (3.09). In total 384 CTX cycles were received. The table shows the results of all intended outcomes. One patient experienced FN after the first cycle of CTX of irinotecan and 5 patients developed 6 FN episodes across all cycles;2 patients were on carboplatin etoposide;1 patient on cisplatin etoposide;1 patient on vinorelbine and 1 patient was on pemetrexed and then on irinotecan CTX when the two FN episodes were developed. Conclusions: This study showing that same day administration of pegfilgrastim was as effective as next day administration in LC patients, without warranting any concerns for febrile neutropenia or delayed engraftment. Utilization of same day pegfilgrastim, in light of biosimilars and COVID, provides a unique opportunity for cancer care without concerns for FN as stated in previous studies. (Table Presented).

Adoption of Satellite Offices in Response to a Pandemic: Sustainability and Infection Control

The office environment has changed rapidly due to the recent COVID-19 outbreak. Companies consider various types of remote work environments to contain the spread of the virus. Among them, a satellite office is a type of remote work environment where a number of employees are allocated to their nearest office. The benefits from satellite offices are twofold: The significant reduction of travel distance also reduces the amount of carbon emission and fuel consumption. In addition, dividing employees into smaller groups significantly reduces the potential risks of infection in the office. This paper addresses a satellite office allocation problem that considers social and environmental sustainability and infection control at work. In order to evaluate the effect of different satellite office allocation, quantitative measures are developed for the following three criteria: carbon emission, fuel consumption, and the probability of infection occurrence at work. Simulation experiments are conducted to investigate different scenarios of regional infection rate and modes of transportation. The results show that adopting satellite offices not only reduces carbon emission and fuel consumption, but also mitigates business disruption in the pandemic.

Your Eyes Show What Your Eyes See (Y-EYES): Challenge-Response Anti-Spoofing Method for Mobile Security Using Corneal Specular Reflections

As the need for contactless biometric authentication becomes more significant during COVID-19, and beyond, the popular biometric authentication method for mobile devices, iris detection, and facial recognition confronts various usability, security, and privacy concerns, including mask-wearing and various Presentation Attacks (PA). Specifically, liveness detection against spoofed artifacts is one of the most challenging tasks as many existing methods cannot conclusively assess the user's physical presence in unsupervised environments. Even though several methods have been proposed for tackling PA with motion challenges and 3D mapping, most of them require expensive depth sensors and fail to detect sophisticated 3D reconstruction attacks. We present a software-based face PA Detection (PAD) method named "Your Eyes Show What Your Eyes See (Y-EYES),"which creates challenges and detects meaningful corneal specular reflection responses from human eyes. To detect human liveness, Y-EYES creates multiple screen image patterns as a challenge, then captures the response of corneal specular reflections using the front camera and analyzes the images using lightweight Machine Learning (ML) techniques. Y-EYES system components include challenge pattern generation, reflection image augmentation (e.g., super-resolution), and ML-based analyses. We have implemented Y-EYES as Android, iOS, and web apps. Our extensive experimental results show that Y-EYES achieves liveness detection with high accuracy at around 200 ms against various types of sophisticated PA. Y-EYES liveness detection can be applied for multiple contactless biometric authentications accurately and efficiently without any costly extra sensors. © 2021 ACM.

Informal Technology Education for Women Transitioning from Incarceration

As society increasingly relies on digital technologies in many different aspects, those who lack relevant access and skills are lagging increasingly behind. Among the underserved groups disproportionately affected by the digital divide are women who are transitioning from incarceration and seeking to reenter the workforce outside the carceral system (women-in-transition). Women-in-transition rarely have been exposed to sound technology education, as they have generally been isolated from the digital environment while in incarceration. Furthermore, while women have become the fastest-growing segment of the incarcerated population in the United States in recent decades, prison education and reentry programs are still not well adjusted for them. Most programs are mainly designed for the dominant male population. Consequently, women-in-transition face significant post-incarceration challenges in accessing and using relevant digital technologies and thus have added difficulties in entering or reentering the workforce. Against this backdrop, our multi-disciplinary research team has conducted empirical research as part of technology education offered to women-in-transition in the Midwest. In this article, we report results from our interviews with 75 women-in-transition in the Midwest that were conducted to develop a tailored technology education program for the women. More than half of the participants in our study are women of color and face precarious housing and financial situations. Then, we discuss principles that we adopted in developing our education program for the marginalized women and participants' feedback on the program. Our team launched in-person sessions with women-in-reentry at public libraries in February 2020 and had to move the sessions online in March due to COVID-19. Our research-informed educational program is designed primarily to support the women in enhancing their knowledge and comfort with technology and nurturing computational thinking. Our study shows that low self-efficacy and mental health challenges, as well as lack of resources for technology access and use, are some of the major issues that need to be addressed in supporting technology learning among women-in-transition. This research offers scholarly and practical implications for computing education for women-in-transition and other marginalized populations. © 2021 Owner/Author.

Study on emotional effects of agro-healing program on COVID-19 trauma among vulnerable groups

As the quarantine life from COVID-19 is prolonged, the number of people suffering from mental and psychological disorders such as fear, anxiety, and depression from viral infection is increasing. Healing agriculture, which mediates agricultural resources, has proven various effects in terms of psychological and emotional aspects. Based on this, the study investigation was conducted during 2020 at Wind Sunshine Farm, Gyeongsangbukdo, South Korea to study the emotional effects on overcoming psychological trauma by conducting an agro-healing program for the selected elderly and housewives of the COVID19 vulnerable class. The following agro-healing program was designed by applying flower therapy, aroma therapy, and food therapy using the five senses and was carried out using plants from the farm. The two groups of housewives and elderly ran the same program for each of the four sessions, making wreaths and flower arrangements using farm resources such as Setaria viridis, Salvia officinalis, Capsella burapastoris, and Gomphrena globosa, planting Rosmarinus officinalis planted in herb gardens in the farm, and harvesting and cooking crops such as Ocimum basilicum, Lycopersicone sculentum, and Lactuca sativa. As an evaluation tool, anxiety (STAI-T) and depression scale (K-BDI) were used to examine emotional changes, and the level of stress was measured by measuring U-BIO MACPA and salivary acidity to determine physiological changes according to emotional function. When looking at the changes in the subjects after the agro-healing program, the degree of anxiety significantly decreased for the housewives (p=.037), but no significant changes within the elderly group. On the other hand, it was found that the degree of depression in the elderly, somewhat higher before implementation was significantly reduced (p=.010). As a result of measuring the stress index to examine physiological changes in emotional function, there was a significant decrease in housewives (p=.047), and no change within the elderly group. As a result of measuring saliva acidity at each session, housewives had a significant effect after implementation in sessions 1, 3, and 4 (p1 =.011, p2 =.083, p3 =.016, p4 =.010), and the elderly had significant effects in the 3rd and 4th sessions (p1 =.140, p2 =.564, p3 =.005, p4 =.007). It is judged that there will be positive effects if a longer-term curative agriculture program is applied to various groups suffering from trauma due to COVID-19. © 2020, Gaurav Society of Agricultural Research Information Centre. All rights reserved.