Evaluation of the Improvements of Transformer Prediction with Convolution

Proposed and developed 5 years ago, Transformer has been a prevailing machine learning method and is widely used to solve various kinds of practical problems [1]. According to relevant works, Transformer has performed well in both natural language processing and computer vision tasks, so we would like to test its effectiveness in prediction, specifically, time series prediction. Over the past two years, COVID-19 is no doubt one of the major factors that influences the changes in the stock prices, and the medical industry should be among the most significantly affected, which would provide an ideal sample for us to study transformer on time series prediction. In this paper, we not only construct a machine learning model using Transformer to predict the stock prices of one medical company but also add a convolution layer to try to optimize the predictions. The comparison of the outcome from the two models suggests that the convolution layer could improve the performance of the naive transformer in several ways. © 2022 IEEE.

A Study on the Customer Buying Behaviour on E-Commerce Websites with Respect to Electronics during Covid-19

During Covid 19, except for essential commodities, all physical outlets were shut, and e-commerce played an important role in catering electronics products to consumers. The factors taken into consideration were Income, Occupation, Education, Gender, Age, and Experience with respect to the customer buying behaviour. An overall study reveals that experience in using e-commerce websites is a major factor influencing customer buying behaviour, and other factors such as income, occupation, education, gender, and age do not have any effect on the buying decision. The inferences are, to a large extent, in the interest of e-commerce providers whose structure of the business solely relies on the behaviour of e-customers. The study attempts to analyze the influence of the extent of consumer buying behaviour during Covid 19 for e-commerce electronics products. This paper tests that e-com shopping was raised due to the coronavirus. © 2023 IEEE.

Detecting the Effect of Internet Skill and e-Leadership on the Productivity of Working from Home

Covid-19 has caused organizations to implement remote work arrangements as the best alternative to limit the spread of the Corona virus. The ability of employees in utilizing internet and also the ability of supervisors in leading work unit remotely are hypothesized as two major factors on work from home (WFH) productivity. This study uses a survey-based quantitative approach to prove the three developed hypotheses. It about 113 governmental employees who have been involved in this study. Perceptual responses were structured using the Structural Equation Modeling and processed using the SmartPLS version 3.3.9 application. The results explain that personal capability of supervisors in utilizing internet as well as in leading work unit is a pivotal matter which determined work productivity and also development of internet skill of governmental employees. © 2022 IEEE.

Effects of emission sources on the particle number size distribution of ambient air in the residential area

Particle size distribution is a major factor in the health and climate effects of ambient aerosols, and it shows a large variation depending on the prevailing atmospheric emission sources. In this work, the particle number size distributions of ambient air were investigated at a suburban detached housing area in northern Helsinki, Finland, during a half-year period from winter to summer of 2020. The measurements were conducted with a scanning mobility particle sizer (SMPS) with a particle size range of 16–698 nm (mobility diameter), and the events with a dominant particle source were identified systematically from the data based on the time of the day and different particle physical and chemical properties. During the measurement period, four different types of events with a dominant contribution from either wood-burning (WB), traffic (TRA), secondary biogenic (BIO), or long-range transported (LRT) aerosol were observed. The particle size was the largest for the LRT events followed by BIO, WB, and TRA events with the geometric mean diameters of 72, 62, 57, and 41 nm, respectively. BIO and LRT produced the largest particle mode sizes followed by WB, and TRA with the modes of 69, 69, 46, and 25 nm, respectively. Each event type had also a noticeably different shape of the average number size distribution (NSD). In addition to the evaluation of NSDs representing different particle sources, also the effects of COVID-19 lockdown on specific aerosol properties were studied as during the measurement period the COVID-19 restrictions took place greatly reducing the traffic volumes in the Helsinki area in the spring of 2020. These restrictions had a significant contribution to reducing the concentrations of NOx and black carbon originating from fossil fuel combustion concentration, but insignificant effects on other studied variables such as number concentration and size distribution or particle mass concentrations (PM1, PM2.5, or PM10). © 2022 The Authors

A Study of COVID-19 Impact on Environmental Health of Three Major Countries

The COVID-19 epidemic has put the majority of the world under lockdown, and one unintended effect of this response has been an improvement in global air quality. The objective of this research is to examine the correlations between pollution levels in air (carbon monoxide, ozone, nitrogen dioxide, particulate matter etc.) and their impact during COVID-19. Our findings state that air pollution can be considered as a major factor in the spread of COVID-19 pandemic. It has an effect on chronic diseases including cardiovascular disease and diabetes, air pollution can also be associated with the increase in COVID-19 severity and lethality. This study shows that air pollution exposure reduces immunological responses, allowing viruses to penetrate and replicate more easily. Various contaminants affected the quality of air as their effects were evaluated during COVID-19 lockdown imposed by the government with the help of different response dataset trackers. ernment response tracker dataset including daily air pollution data and weather data from several cities in the United States, India, and Switzerland. We have used data from (CAAQMS) Continuous Ambient Air Quality Monitoring Stations, to conduct a detailed examination of the COVID-19's effect on the quality of air and reported changes in Air Quality Index (AQI). The observation indicates certain contaminants NO2, PM2.5 other factors, too, have a considerable influence in COVID-19 infection. © 2022 IEEE.