Predicting Aramco's IPO Long-Term Performance During COVID Times

This paper aims to assess whether the outbreak of the highly contagious pandemic had an impact on the share prices of recently listed Aramco in light of the Fads hypothesis using the methods of neural network and ARIMA. The IPO of Aramco, the world's largest oil company, was a much-hyped affair. Given the relevant importance of the company, it was expected that Aramco's share prices would not underperform in the long run. But the analysis indicates the opposite. The study uses two time periods using the announcement of the pandemic by the World Health Organization as the threshold date to see the impact of the pandemic on Aramco's share prices. The forecasting results validate the Fads hypothesis implying that Aramco's share prices would have underperformed in the long run, even in the absence of a pandemic outbreak. Finally, the study cautions investors against the hype created by IPOs. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Forecasting of COVID-19 Outbreak in India: A Time Series Analysis

The World Health Organization (WHO) declared the status of coronavirus disease 2019 (COVID-19) to a global pandemic on March 11, 2020. Since then, numerous statistical, epidemiological and mathematical models have been used and investigated by researchers across the world to predict the spread of this pandemic in different geographical locations. The data for COVID-19 outbreak in India has been collated on daily new confirmed cases from March 12, 2020 to April 10, 2021. A time series analysis using Auto Regressive Integrated Moving Average (ARIMA) model was used to investigate the dataset and then forecast for the next 30-day time-period from April 11, 2021, to May 10, 2021. The selected model predicts a surge in the number of daily new cases and number of deaths. An investigation into the daily infection rate for India has also been done. © 2023 The authors and IOS Press.

Gender Diversity and Firm Profitability, before and during the Covid-19 Pandemic

The intense competition in business will lead company managers to ensure their business is performing at its best. The board of directors' choice will always impact whether the company's value rises or falls. The argument that companies with female executives make better judgments for shareholders has led to a noticeable trend of raising the participation of women on boards in several nations throughout the world during the past decade. Male directors tend to be risk-takers, whereas female directors are more risk-averse, making them more effective decision-makers in some situations. When making complicated decisions, women on the board typically digest information more effectively and efficiently than a board made up entirely of men. This study examines if having more women on boards of directors increases company profitability. This study examines how gender diversity influences the impact of the board of directors (BOD) and independent directors (ID) on business profitability throughout two time periods - before and during COVID-19 - Pandemic. This study's sample comprises 40 Food and Beverage firms listed on the Indonesian Capital Market between 2011 and 2021. The F & B industry was chosen as a research topic because it is considered to be able to survive in difficult times. Panel data regression was used to estimate the research models. Before Covid 19 - Pandemic, BOD size significantly negatively influenced firm profitability, whereas ID positively impacted firm profitability. The presence of women on the board significantly mitigates the negative impact of the BOD on firm profitability. During the Pandemic, BOD had no significant impact on corporate profitability. Nonetheless, ID has a negative impact on company profitability. Surprisingly, this condition demonstrates that the presence of women on the BOD strengthened the influence of the BOD and ID on firm profitability. Increasing women's participation on boards of directors is one way to enhance their performance. © 2023 IEEE.

Analysis of the Impact of COVID-19 on the Development of National ICT Industry

The raging of COVID-19 has caused a huge impact on all countries. This paper selects China, which has adopted a "strict strategy” in response to the epidemic, to observe the correlation between changes in COVID-19 data and ICT statistics, so as to analyze the impact of COVID-19 on the ICT industry. Due to availability of the data, this paper mainly analyzes the impact on telecommunication industry, mobile Internet, Internet business and software industry, which are more consumption-oriented in the ICT industry. In this paper, data from different fields at different time periods are collected and organized into four sets of graphs, and each graph is analyzed using pearson correlation data model and simple linear regression model. It can be concluded that the revenue of ICT industry in different fields was affected differently during the epidemic period. The specific impact needs to be discussed according to the different types of business in relation to the development of the epidemic. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

STORM-GAN: Spatio-Temporal Meta-GAN for Cross-City Estimation of Human Mobility Responses to COVID-19

Human mobility estimation is crucial during the COVID-19 pandemic due to its significant guidance for policymakers to make non-pharmaceutical interventions. While deep learning approaches outperform conventional estimation techniques on tasks with abundant training data, the continuously evolving pandemic poses a significant challenge to solving this problem due to data non-stationarity, limited observations, and complex social contexts. Prior works on mobility estimation either focus on a single city or lack the ability to model the spatio-temporal dependencies across cities and time periods. To address these issues, we make the first attempt to tackle the cross-city human mobility estimation problem through a deep meta-generative framework. We propose a Spatio-Temporal Meta-Generative Adversarial Network (STORM-GAN) model that estimates dynamic human mobility responses under a set of social and policy conditions related to COVID-19. Facilitated by a novel spatio-temporal task-based graph (STTG) embedding, STORM-GAN is capable of learning shared knowledge from a spatio-temporal distribution of estimation tasks and quickly adapting to new cities and time periods with limited training samples. The STTG embedding component is designed to capture the similarities among cities to mitigate cross-task heterogeneity. Experimental results on real-world data show that the proposed approach can greatly improve estimation performance and outperform baselines. © 2022 IEEE.

COVID-19 Detection Using CNN-ResNet-50 Model

COVID-19 is a deadly virus that originated in 2019 and could be easily transmitted from one geographical area to another. It affected the integral world, resulting in severe mortality due to its contagious effect on human life. The infection rate is continuously growing and it is becoming unmanageable since the virus moves easily from one human to another. Once we detect the COVID-19 virus in its early stages, we can easily reduce the death rate. The most common and widely used method of diagnosing COVID is through reverse transcription polymerase chain (RT-PCR). But the RT-PCR test is time consuming, inaccurate, and expensive. In this situation, the time period for the detection of viruses is valuable. Keeping these limitations in mind, we use an X-ray image of the chest to identify the COVID-19 infected patient. This procedure is achieved by using convolution neural network (CNN) in deep learning. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Visualizing Temporal Data using Time-dependent Non-decreasing Monotone Functions

Ahstract-The occurrence of seasonal natural phenomena depends on the conditions leading to it and not directly on the progression of time, meaning its context varies across time and space. Examples of this include comparing plant growth, insect development or wildfire risk during the same time period at different locations or in different time periods at the same location. However, visualizing and comparing such phenomena usually implies plotting it across the time axis as it's perceived as temporal data. Since it's not directly dependent of time, identifying patters of recurrence using this technique is inefficient. Because of this, we proposed transforming (when needed) the dependent function to a non-decreasing monotone one, in order to preserve the monotonic property of time progression. Then we used the resulting function as a time axis replacement to achieve an equal ground of comparison between the different contexts in which the phenomenon occurs. We applied this technique to real data from seasonal natural phenomena, such as plant and insect growth, to compare its progression in different temporal and spatial contexts. Since the dependent function of the phenomenon was scientifically known, we were able to directly use the technique to infer its seasonality patterns. Furthermore, we applied the technique to real data from the coronavirus worldwide pandemic by hypothesizing its dependent function and analysing if it was able to reduce the existing temporal misalignment between different contexts, like years and countries. The results achieved were positive, although not as remarkable as when the dependent function was known. © 2022 IEEE.

Persistence On Tourism In North Macedonia: The Impact Of COVID-19

This study analyzes the level of tourism persistence in the North Macedonia through predictors of foreign arrivals and overnight stays for the time period of annual data from 1956 to 2020 and for monthly data from January 2010 to October 2021 by applying fractional integration techniques. The results show that for the annual data shocks are temporary by applying autocorrelation model. However, at the monthly data the shocks are expected to have permanent effects. The government should react further in trying to bring back the tourism figures as before the pandemic COVID-19. Copyright © 2022 The Authors.

Impact of Vaccination and Testing in an Urban Campus Model for the SARS-CoV-2 Pandemic

The crisis induced by the Coronavirus pandemic severely impacted educational institutes. Even with vaccination efforts underway in 2021, it was not clear that sufficient confidence will be achieved for schools to reopen soon. This paper considers the impact of testing rates in addition to vaccination rates in order to reduce infections and hospitalizations and evaluates strategies that allow educational institute in urban settings to remain open. These strategies are also applicable to big campus style businesses and would help planning to keep the businesses open and help the economy. Our analysis is based on a graph model where nodes represent population groups and edges represent population exchanges due to commuting populations. The commuting population is associated with edges and is associated with one of the end nodes of the edge during part of the time period and with the other node during the remainder of the time period. The progression of the disease at each node is determined via compartment models, that include vaccination rates and testing to place infected people in quarantine along with consideration of asymptomatic and symptomatic populations. Applying this to a university population in Chicago with a substantial commuter population, chosen to be 80% of the school's population as an illustration, provides an analysis which specifies benefits of testing and vaccination strategies over a time period of 150 days. © 2022 Owner/Author.

A Comparison of the Academic Performance of Rising Scholars with Other Student Demographic Groupings before and during the COVID Pandemic

The Purdue University Rising Scholars program was established in 2016 by a NSF grant designed to examine the effect of adult mentor support networks on student performance. The first students began classes in the fall of 2017, and their performance and many aspects of the program have been reported in the literature. Unfortunately, during this same time period, the COVID-19 pandemic moved across the globe and dramatically changed collegiate education. The effects of the pandemic in education will be felt for some time following the eventual demise of the virus. Because of this NSF grant period, the research team was uniquely positioned with matched pair sets of matriculating students from the Rising Scholars program, engineering, and exploratory studies. This paper will compare the performance of these students and the general student population for GPA and retention between the pre-COVID period (< spring of 2020) and the COVID period (spring 2020 onward). It is commonly perceived among collegiate instructors that student performance has suffered during the pandemic. The Rising Scholar demographic has the potential to have increased adverse effects from the pandemic disruption, but they also have an established adult mentor support network. The researchers have looked at differential performance outcomes between the various groups and exposed a tendency toward diminished performance with thinner networked students. Sample sizes were too small for the evaluation of any meaningful statistical tests. © 2022 ASABE. All Rights Reserved.

Ambient Fine Particulate Matter and COVID-19 in India

We examine the correlation between COVID-19 case activity and air pollution in two cities of Delhi and Mumbai in India. Data regarding air quality index (AQI) of PM2.5 and PM10 from Delhi and Mumbai were collected between July and November 2020. Within the same time period, confirmed cases and daily deaths due to COVID-19 in these two cities were also recorded. AQI levels in Delhi were worst in November (PM2.5: 446 ± 144.6 µg/m3;PM10: 318 ± 131.7 µg/m3) and were significantly higher as compared to Mumbai (PM2.5: 130 ± 41.2 µg/m3;PM10: 86 ± 21.2 µg/m3). This correlated with greater number of cases and higher mortality in Delhi (cases: 6243;deaths: 85) relative to Mumbai (cases: 1526;deaths: 35) during the same time period. This observational study shows that air pollution is associated with poor outcomes in patients with COVID-19. There is an urgent unmet need for appropriate public health measures to decrease air pollution along with strict policy change. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Comparison of Variations between the Waves in This COVID-19 Pandemic: Past, Present and Future

The COVID-19 pandemic started by the SARS-CoV-2 virus from China hit different parts of the world and caused till now the first, second and third waves at different time periods from March 2020 to December 2021. Virus variants emerged to cause these waves with altered behaviour and severity of the disease and difficulty in the management of the pandemic. An unexpected upsurge happened during these waves due to social reasons and policies. In this article, we discuss the variations in the waves from a few geographic locations which will give us a better understanding of regional effects and precautions needed for the future. © 2022 Society for Biomaterials and Artificial Organs - India. All rights reserved.

Comparison of International Stock market Volatility: An Empirical Analysis during Economic Crisis 2008 and Covid-19

The repercussions occasionally are more detrimental than the storm. Similar is the scenario of the Economic Crisis 2008. It was devastating at the moment, but the concern was more towards the time period it has prolonged in the economy. In mid-March 2020, when our honourable Prime Minister announced about the entire nation lockdown, Covid-19 was proven to be a pandemic that not only deteriorated the health of the nation but also was a revolutionary change for the economy as well. Hence, estimating volatility in such dreadful scenarios becomes extremely important. Earlier researchers have made an attempt to study the impact of these two devastating events individually. However, this study makes an attempt to provide a comparative analysis of the volatility of Economic Crisis 2008 and Covid 19 in order to observe the nature of volatility in two different events of top 10 economies which comprises 66% of the whole world's GDP and accordingly can be taken as intermediary to address the world's economy.The data has been bifurcated into two sections, first comprises of Economic Crisis 2008 Period ranging from 1st of January 2008 to 31st of May 2011. While second section comprises of Covid-19 Period ranging from January 1, 2020 to May 12, 2021. In order to estimate the volatility and effect GARCH, T-GARCH and E-GARCH models have been applied for the fulfilment of the objective. The results provide with the conclusion that there has been significant impact of both the events on the volatility of the stock market.The study can be beneficial for the financial analysts and, retail and individual investors in order to make a prudent decision in such extremities as it presents with an insight of how different economies react to different circumstances correspondingly. © The Electrochemical Society

Investigating The Effect of Covid Induced Lockdown On Land Surface Temperature Over Ahmedabad City

The present study focuses over Ahmedabad City of Gujarat State, India for the time period 1st March to 30th June comprising of the Pre-Lockdown Phase (PLP), the National Lockdown Phase - 1 (NLP1) and the Unlock Phase - 1 (ULP1). We have considered this time period over the years 2019, 2020 and 2021 to explore the effect of COVID induced lockdown on LST and understanding its variation. Satellite data acquired from AQUA - MODIS with a spatial and temporal resolution of 1 Km and 1-2 days respectively was used for the analysis of the LST. The average LST over Ahmedabad was 314.18 K, 311.79 K and 315.67 K for PLP over the years 2019, 2020 and 2021. For NLP1 the average LST over those years were 321.68 K, 318.73 K and 319.39 K respectively. And for the ULP1 the average LST over those years were 319.87 K, 314.07 K and 312.19 K respectively. We observe a 2.38 %, 2.22 % and 1.17 % increase in LST from the PLP to NLP1 during the years 2019, 2020 and 2021. The increase of LST during the NLP1 in 2020 showed that as the pollution decreased, the active elements that were present in the atmosphere which caused disturbance to the sensor on the satellite while calculating LST were reduced and we got a brighter top of surface. The decrease in LST from 2019 levels for the ULP1 is also observed indicating the effects of lockdown and onset of monsoon in 2020 and 2021. © 2021 IEEE.

Assessing the Causal Impact of COVID-19 Related Policies on Outbreak Dynamics: A Case Study in the US

Analyzing the causal impact of different policies in reducing the spread of COVID-19 is of critical importance. The main challenge here is the existence of unobserved confounders (e.g., vigilance of residents) which influence both the presence of policies and the spread of COVID-19. Besides, as the confounders may be time-varying, it is even more difficult to capture them. Fortunately, the increasing prevalence of web data from various online applications provides an important resource of time-varying observational data, and enhances the opportunity to capture the confounders from them, e.g., the vigilance of residents over time can be reflected by the popularity of Google searches about COVID-19 at different time periods. In this paper, we study the problem of assessing the causal effects of different COVID-19 related policies on the outbreak dynamics in different counties at any given time period. To this end, we integrate COVID-19 related observational data covering different U.S. counties over time, and then develop a neural network based causal effect estimation framework which learns the representations of time-varying (unobserved) confounders from the observational data. Experimental results indicate the effectiveness of our proposed framework in quantifying the causal impact of policies at different granularities, ranging from a category of policies with a certain goal to a specific policy type. Compared with baseline methods, our assessment of policies is more consistent with existing epidemiological studies of COVID-19. Besides, our assessment also provides insights for future policy-making. © 2022 ACM.

Actual Practice of Short Time Period Implementation of MultipleWeb Conferencing Systems for Online Classes

With the expansion of COVID-19 outbreaks, the lecture environment has changed dramatically. Various activities have been held by distance learning, however, the style of online learning and hybrid learning is also popular. The key to these lectures is the web conferencing tool. The key factor to success in new age lectures is how the web conferencing tool can be adapted to the existing information system. Tokyo University of Agriculture and Technology (TUAT) has developed full online courses from the first semester (spring semester) in April 2020. However, the increase of the infection has remained constant, and TUAT will continue to provide online courses using the web conferencing tool from the second semester (fall semester) in October 2020, and hybrid courses that combine face-to-face teaching. To support this, a campus-wide license was purchased that enables the use of three different web conferencing tools including Zoom Meeting, Cisco Webex, and Google Meet in the suitable applications. For the start of the second semester, the Information Media Center (IMC) has developed a system to integrate with the existing authentication system to enable use of the these three tools. Since we didn't have enough time to implement the system integration, we need to take less than a month (about 3 weeks) from the contract to the start of service. In this presentation, we explain in detail how we designed the system and how it was implemented in order to deploy these systems in a very short time period. We will also mention the implementation issues we faced. By explaining the ideal and reality of the system, we would like to discuss together with the SIGUCCS community how the system should be designed for the unusual situation. © 2022 ACM.

Forecasting Evictions to Determine Areas at High Risk of Housing Insecurity

The COVID-19 pandemic has contributed to an escalating housing crisis in the United States. After the onset of pandemic-related lockdowns in March of 2020, eviction morato-riums were swiftly enacted, enabling millions of tenants in rental residences to remain in their homes while unemployment surged and families lacked the resources to pay rent. With the majority of these moratoriums scheduled to lift by the end of 2021, many tenants will face imminent eviction. This paper outlines the development of a multivariable, time-series regression model that can be used to forecast eviction rates as a function of changing economic conditions in a given geographical area and time period. When the model is applied to New Jersey in the current time period, the results reveal a buildup of evictions, which upon the lifting of the eviction moratorium will significantly intensify the existing housing insecurity crisis. © 2021 IEEE.

Sensing the Impact of COVID-19 Restrictions from Online Reviews: The Cases of London and Paris Unveiled Through Text Mining

This study aims to understand how the COVID-19 pandemic affected the hotel sector and to identify the current traveler demands. The traveler’s reviews were analyzed based on sentiment analysis and a guest satisfaction model was also proposed, demonstrating a data mining approach within tourism and hospitality research. Given its popularity, TripAdvisor was the chosen platform for collection of hotel reviews in London and Paris. Text data were extracted from reviews made in two time periods, before and during the COVID-19 pandemic. The sentiment and specific aspects highlighted by travelers were compared between each period. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Emotion Mining from Speech in the Covid-19 Era: An Exploratory Study

Speech is the most effective form of communication because it is not limited to just the linguistic components but carries the speaker's emotions laced within the components like tone of voice and cues like cries and sighs. This paper aims at studying the research done in the past and applying it to the Covid-19 era.The pandemic is of a great magnitude, affecting every aspect of life including emotions. This time period requires research in determining the most dominant emotions in conversations, to serve as a reference for future research and as a contrast to the research done in the past. Previous papers have identified emotions like happiness, anger, fear and sadness using feature extraction algorithms like MFCC (Mel Frequency Cepstral Coefficients and numerous classification algorithms like GMM (Gaussian Mixture Model), SVM (Support Vector Machine), KNN (K-Nearest-neighbor) and HMM (Hidden Markov Model). Some research has pointed towards ASR (Automatic Speech Recognition), N-Grams and vector space modeling. This paper aims at recognizing the most suitable algorithms for determining the pandemic specific emotions in speech. © 2021 IEEE.

Utilizing Smart City Cyber-physical Infrastructure for Tracking and Monitoring Pandemics like COVID-19 with the ICCC as the Nerve Centre

Managing the ongoing COVID-19 (aka Coronavirus) pandemic has presented both challenges and new opportunities for urban local body administrators. With the Indian government's Smart City mission taking firm roots in some of the Indian cities, the authors share their learnings and experiences of how a Smart City Integrated Command and Control Centre (ICCC) can be extended to become the nerve centre of pandemic-related operations and management, leveraging the Smart City IoT infrastructure such as surveillance cameras for monitoring and enforcement. The authors are of the opinion that the lessons learned and experiences gained from these cities are extremely valuable and can easily be replicated in other cities in a relatively short time period, thus providing a standard and uniform method across the nation for handling epidemics in the future. © 2020 ACM.