Changes of Air Pollutants in Urban Cities during the COVID-19 Lockdown-Sri Lanka

In response to the COVID-19 pandemic in early 2020, Sri Lanka underwent a nationwide lockdown that limited motor vehicle movement, industrial operations, and human activities. This study analyzes the impact of COVID-19 lockdown on carbon monoxide (CO), ozone (O3), nitrogen dioxide (NO2), sulfur dioxide (SO2), and particulate matter (PM10, PM2.5) concentrations in two urban cities (Colombo and Kandy) in Sri Lanka, by comparison of data from the lockdown period (March to May 2020) with its analogous period of 2019 and 2021. The results showed that the percentage change of daytime PM10, PM2.5, CO, and NO2 concentration during the lockdown in Colombo (Kandy) is –42.3% (–39.5%), –46% (–54.2%), –14.7% (–8.8%) and –82.2% (–80.9%), respectively. In both cities, the response of NO2 to the lockdown was the most sensitive. In contrast, daytime O3 concentration in Colombo (Kandy) has increased by 6.7% (27.2%), suggesting that the increase in O3 concentration was mainly due to a reduction in NOx emissions leading to lower O3 titration by NO. In addition, daytime SO2 concentration in Colombo has increased by 22.9%, while daytime SO2 concentration in Kandy has decreased by –40%. During the lockdown period, human activities were significantly reduced, causing significant reductions in industrial operations and transportation activities, further reducing emissions and improving air quality in two cities. The results of this study offer potential for local authorities to better understand the emission sources, assess the effectiveness of current air pollution control strategies, and form a basis for formulating better environmental policies to improve air quality and human health. © The Author(s).

Urban drone adoption: Addressing technological, privacy and task–technology fit concerns

Cutting-edge technologies are changing the operations of urban last-mile delivery. In particular, innovative technologies, such as delivery drones, have shown promising results in commercial applications. When considered alongside the ongoing pandemic, contactless technologies have become even more important to the daily lives of consumers in highly urbanized areas. This study investigates underlying factors influencing consumers' acceptance of drone delivery in urban cities amidst the COVID-19 pandemic. To this end, a model was created by fusing the technology acceptance model, task–technology fit, and privacy calculus theory. Four hundred and fifty survey responses were analyzed using structural equation modeling. The findings suggested that perceived usefulness, attitude, and perceived privacy risks directly influence consumers' behavioral intentions. In addition, perceived ease of use, task characteristics, technology characteristics, task–technology fit, and privacy concerns indirectly impact consumers' behavioral intention. This study offers an insightful perspective on consumers' perception of urban last-mile delivery drones while providing insights into urban transport planning and regulation of drone delivery services. © 2023 Elsevier Ltd

Urban drone adoption: Addressing technological, privacy and task–technology fit concerns

Cutting-edge technologies are changing the operations of urban last-mile delivery. In particular, innovative technologies, such as delivery drones, have shown promising results in commercial applications. When considered alongside the ongoing pandemic, contactless technologies have become even more important to the daily lives of consumers in highly urbanized areas. This study investigates underlying factors influencing consumers' acceptance of drone delivery in urban cities amidst the COVID-19 pandemic. To this end, a model was created by fusing the technology acceptance model, task–technology fit, and privacy calculus theory. Four hundred and fifty survey responses were analyzed using structural equation modeling. The findings suggested that perceived usefulness, attitude, and perceived privacy risks directly influence consumers' behavioral intentions. In addition, perceived ease of use, task characteristics, technology characteristics, task–technology fit, and privacy concerns indirectly impact consumers' behavioral intention. This study offers an insightful perspective on consumers' perception of urban last-mile delivery drones while providing insights into urban transport planning and regulation of drone delivery services.

Kingdom of Saudi Arabia: Era of Smart Cities

With the movement of people from the countryside to urban cities, the need to develop services increases as well. The development of services is necessary to accommodate the population growth in terms of transportation, education, and health. Smart cities meet these issues using a comprehensive echo system incorporating smart technologies and enhancing citizens' quality of life. In this paper, we focus on six dimensions of smart cities: living, environment, governance, economy, people, and transportation. In light of these dimensions, we review six smart cities around the world: Dubai, Tokyo, Singapore, Hong Kong, Seoul, and Reykjavik. Moreover, we review Riyadh, Saudi Arabia, as a city aspiring to be fully smart in each dimension. Lastly, we elaborate on the health aspect with regard to the response to the COVID-19 pandemic in all cities under consideration. © 2022 IEEE.

Depression Detection in Tweets from Urban Cities of Malaysia using Deep Learning

This document was inspired by how the usage of social media platforms in Malaysia such as Twitter have drastically increased ever since the recent Covid-19 pandemic. While practicing social distancing and other pandemic regulations was for the betterment and prevention of physical health, mental health of most was affected negatively. People generally revolve around with having interactions with other humans and once the physical form of it was cut, people tend to turn to social media. A twitter sentiment analysis approach was used to find the casual link between social media and mental health. This project aims to utilise the broaden scope of social media-based mental health measures since research proves the evidence of a link between depression and specific linguistic features as well. Therefore, the research entails on how the problem statement of this project on developing an algorithm that can predict text- based depression symptoms using deep learning and Natural Language Processing (NLP) can be achieved. The objective of the project is to identify depressive tweets using NLP and Deep Learning in the urban cities of Malaysia within the beginning of the Covid-19 period to enable individuals, their caregivers, parents, and even medical professionals to identify the linguistic clues that point towards to signs of mental health deterioration. Additionally, this paper also researches to make the proposed system to identify words that represent depression and categorize them accordingly as well as improve the accuracy of the system in identifying tweets that display the depression related words based on its specific location. This objective will be achieved following the methodology using the Deep Learning approach and Natural Language Processing technique. A recurrent neural network approach was implemented in this project known as the Long-Term Short Memory, which is a form of advanced RNN, that allows information to be preserved. Conducting an analysis on the linguistic indicators from tweets allows for a low-profile assessment that can supplement traditional services which then consequently would allow for a much earlier detection of depressive symptoms. Since this research entails on finding the link between tweets and machine learning's ability to detect depressive symptoms, the success this project brings forth a meaningful help towards those who are mentally affected but are unable to seek help or are unsure on diagnosing themselves as this project helps alert the government and psychologist on the need for it. The project thus far has an accuracy rate of 94%, along with, precision rate of 0.94, recall of 0.96 and an F1 score of 0.95. © 2021 IEEE.

Correlation between seismic noise variation and COVID-19 pandemic measures using recordings from Penn State FORESEE array

Quantifying the response of human activities to different COVID-19 measures may serve as a potential way to evaluate the effectiveness of the measures and optimize measures. Recent studies reported that seismic noise reduction caused by less human activities due to COVID-19 lockdown had been observed by seismometers. However, it is difficult for current seismic infrastructure in urban cities to characterize spatiotemporal seismic noise during the postCOVID-19 lockdown because of sparse distribution. Here we show key connections between progressive COVID-19 measures and spatiotemporal seismic noise changes recorded by a distributed acoustic sensing (DAS) array deployed in State College, PA. Our results shows that DAS recordings using city-wide fiber optics could provide a way for quantifying the impact of COVID-19 measures on human activities in city blocks. © 2021 Society of Exploration Geophysicists First International Meeting for Applied Geoscience & Energy