The Coronavirus pandemic has created complex challenges and adverse circumstances. This research discovers public sentiment amidst problematic socioeconomic consequences of the lockdown, and explores ensuing four potential sentiment associated scenarios. The severity and brutality of COVID-19 have led to the development of extreme feelings, and emotional and mental healthcare challenges. This research identifies emotional consequences - the presence of extreme fear, confusion and volatile sentiments, mixed along with trust and anticipation. It is necessary to gauge dominant public sentiment trends for effective decisions and policies. This study analyzes public sentiment using Twitter Data, time-aligned to COVID-19, to identify dominant sentiment trends associated with the push to 'reopen' the economy. Present research uses textual analytics methodologies to analyze public sentiment support for two potential divergent scenarios - an early opening and a delayed opening, and consequences of each. Present research concludes on the basis of exploratory textual analytics and textual data visualization, that Tweets data from American Twitter users shows more trust sentiment support, than fear, for reopening the US economy. With additional validation, this could present a valuable time sensitive opportunity for state governments, the federal government, corporations and societal leaders to guide the nation into a successful new normal future.

Along with the Coronavirus pandemic, another crisis has manifested itself in the form of mass fear and panic phenomena, fuelled by incomplete and often inaccurate information. There is therefore a tremendous need to address and better understand COVID-19's informational crisis and gauge public sentiment, so that appropriate messaging and policy decisions can be implemented. In this research article, we identify public sentiment associated with the pandemic using Coronavirus specific Tweets and R statistical software, along with its sentiment analysis packages. We demonstrate insights into the progress of fear-sentiment over time as COVID-19 approached peak levels in the United States, using descriptive textual analytics supported by necessary textual data visualizations. Furthermore, we provide a methodological overview of two essential machine learning classification methods, in the context of textual analytics, and compare their effectiveness in classifying Coronavirus Tweets of varying lengths. We observe a strong classification accuracy of 91% for short Tweets, with the Naive Bayes method. We also observe that the logistic regression classification method provides a reasonable accuracy of 74% with shorter Tweets, and both methods showed relatively weaker performance for longer Tweets. This research provides insights into Coronavirus fear sentiment progression, and outlines associated methods, implications, limitations and opportunities.

Recently, the Third Generation Partnership Project (3GPP) introduced the New Radio (NR) technology for Vehicle-to-everything (V2X) communication to enable delay-sensitive and bandwidth-hungry applications in vehicular communication. The NR system is strategically crafted to complement the existing Long Term Evolution (LTE) Cellular-Vehicle-to-Everything (C-V2X) infrastructure, particularly to support advanced services such as the operation of automated vehicles. It is widely anticipated that the 5th generation (5G) NR system will surpass LTE C-V2X in terms of achieving superior performance in scenarios characterized by high throughput, low latency, and enhanced reliability, especially in the context of congested traffic conditions and a diverse range of vehicular applications. This article will provide a comprehensive literature review on vehicular communications from Dedicated Short Range Communication (DSRC) to NR V2X. Subsequently, it delves into a detailed examination of the challenges and opportunities inherent in NR V2X technology. Finally, we proceed to elucidate the process of creating and analyzing an open-source 5G NR V2X module in network simulation-3 (ns-3) and then demonstrate the NR V2X performance in terms of different key performance indicators implemented through diverse operational scenarios.

Investigating and classifying sentiments of social media users (e.g., positive, negative) towards an item, situation, and system are very popular among the researchers. However, they rarely discuss the underlying socioeconomic factor associations for such sentiments. This study attempts to explore the factors associated with positive and negative sentiments of the people about reopening the economy, in the United States (US) amidst the COVID-19 global crisis. It takes into consideration the situational uncertainties (i.e., changes in work and travel pattern due to lockdown policies), economic downturn and associated trauma, and emotional factors such as depression. To understand the sentiment of the people about the reopening economy, Twitter data was collected, representing the 51 states including Washington DC of the US. State-wide socioeconomic characteristics of the people (e.g., education, income, family size, and employment status), built environment data (e.g., population density), and the number of COVID-19 related cases were collected and integrated with Twitter data to perform the analysis. A binary logit model was used to identify the factors that influence people toward a positive or negative sentiment. The results from the logit model demonstrate that family households, people with low education levels, people in the labor force, low-income people, and people with higher house rent are more interested in reopening the economy. In contrast, households with a high number of members and high income are less interested to reopen the economy. The accuracy of the model is good (i.e., the model can correctly classify 56.18\% of the sentiments). The Pearson chi2 test indicates that overall this model has high goodness-of-fit. This study provides a clear indication to the policymakers where to allocate resources and what policy options they can undertake to improve the socioeconomic situations of the people and mitigate the impacts of pandemics in the current situation and as well as in the future.

The ongoing COVID-19 global pandemic is affecting every facet of human lives (e.g., public health, education, economy, transportation, and the environment). This novel pandemic and citywide implemented lockdown measures are affecting virus transmission, people’s travel patterns, and air quality. Many studies have been conducted to predict the COVID-19 diffusion, assess the impacts of the pandemic on human mobility and air quality, and assess the impacts of lockdown measures on viral spread with a range of Machine Learning (ML) techniques. This review study aims to analyze results from past research to understand the interactions among the COVID-19 pandemic, lockdown measures, human mobility, and air quality. The critical review of prior studies indicates that urban form, people's socioeconomic and physical conditions, social cohesion, and social distancing measures significantly affect human mobility and COVID-19 transmission. During the COVID-19 pandemic, many people are inclined to use private transportation for necessary travel purposes to mitigate coronavirus-related health problems. This review study also noticed that COVID-19 related lockdown measures significantly improve air quality by reducing the concentration of air pollutants, which in turn improves the COVID-19 situation by reducing respiratory-related sickness and deaths of the people. It is argued that ML is a powerful, effective, and robust analytic paradigm to handle complex and wicked problems such as a global pandemic. This study also discusses policy implications, which will be helpful for policymakers to take prompt actions to moderate the severity of the pandemic and improve urban environments by adopting data-driven analytic methods.

The fifth Generation (5G) mobile networks use millimeter Waves (mmWaves) to offer giga bit data rates. However, unlike microwaves, mmWave links are prone to user and topographic dynamics. They easily get blocked and end up forming irregular cell patterns for 5G. This in turn cause too early, too late, or wrong handoffs (HOs). To mitigate HO challenges, sustain connectivity and avert unnecessary HO, we propose a HO scheme based on Jump Markov Linear System (JMLS) and Deep Reinforcement Learning (DRL). JMLS is widely known to account for abrupt changes in system dynamics. DRL likewise emerges as an artificial intelligence technique for learning highly dimensional and time-varying behaviors. We combine the two techniques to account for time-varying, abrupt, and irregular changes in mmWave link behaviour by predicting likely deterioration patterns of target links. The prediction is optimized by meta training techniques that also reduces training sample size. Thus, the JMLS-DRL platform formulates intelligent and versatile HO policies for 5G. Results show our proposed prediction scheme about target link behavior post HO to be highly reliable. The scheme also averts unnecessary HOs thus ably supports longer dew time.

There exists a compelling need to better understand the temporal dynamics of public sentiment towards COVID-19 vaccines in the US on a national and state-wise level for facilitating appropriate public policy applications. Our analysis of social media data from early February of 2021 and late March of 2021 shows that in spite of overall strength of positive sentiment, and increasing numbers of Americans being fully vaccinated, negative sentiment about COVID-19 vaccines still persists among sections of people who are hesitant towards the vaccine. In this study, we performed sentiment analytics on vaccine tweets, studied changes in public sentiment over time, conducted vaccination sentiment validation using actual vaccination data from the US CDC and Household Pulse Survey (HPS), explored influence of maturity of Twitter user-accounts and generated geographic mapping of sentiments by location of Twitter users. Furthermore, we leverage the emotion polarity based Public Sentiment Scenarios (PSS) framework which was developed for COVID-19 sentiment analytics, to systematically analyze directions for public policy processes to potentially improve the administration of vaccines. Application of the PSS framework provides important time sensitive insights for state and federal government agencies and associated organizations to better implement public policy processes for healthcare management, communication, transparency, motivation and societal operational policies such as social distancing. These insights are expected to contribute to processes that can expedite the vaccination program and move closer to the cherished herd immunity goal.