Impact of COVID-19 on the US and Texas Economy: A General Equilibrium Approach

This paper examines the impact of COVID-19 on the US and Texas economy using a computable general equilibrium model, REMI PI+. We consider three scenarios based on economic forecasts from various sources, including the University of Michigan’s RSQE (Research Seminar in Quantitative Economics), IMF, and the Wi orld Bank. We report a GDP loss of $106 million (a 6% decline) with 1.2 million jobs lost (6.6%) in Texas in 2020. At the national level, GDP loss is $996 billion (a 5% decline) with 11.5 million jobs lost (5.5%) in the same year. By 2026, the aggregate total GDP loss in Texas ranges from $378 to $629 million. The estimated unemployment rate in Texas in 2021 ranges from 5% to 7.7%, depending on modeling assumptions. The granularity of the CGE results allow examination of the most and least impacted industries. Health Care and Social Assistance, Construction, and Accommodation and Food Services incur the most job loss while State and Local Government and Farm will likely see an increase in jobs for 2020. These insights separate our work from most current impact studies.

New Computational Methods and the Study of the Romance Languages

Nowadays, computer models of human language are instrumental to millions of people, who use them every day with little if any awareness of their existence and role. Their exponential development has had a huge impact on daily life through practical applications like machine translation or automated dialogue systems. It has also deeply affected the way we think about language as an object of scientific inquiry. Computer modeling of Romance languages has helped scholars develop new theoretical frameworks and new ways of looking at traditional approaches. In particular, computer modeling of lexical phenomena has had a profound influence on some fundamental issues in human language processing, such as the purported dichotomy between rules and exceptions, or grammar and lexicon, the inherently probabilistic nature of speakers’ perception of analogy and word internal structure, and their ability to generalize to novel items from attested evidence. Although it is probably premature to anticipate and assess the prospects of these models, their current impact on language research can hardly be overestimated. In a few years, data-driven assessment of theoretical models is expected to play an irreplaceable role in pacing progress in all branches of language sciences, from typological and pragmatic approaches to cognitive and formal ones.

Shifting the Blame: Storm and Wildfire Dramatic Images in American News Media

Some media frames might be likely to seek to evoke a certain sentiment, and that natural disaster coverage by the media focuses on the current impact of disasters. In their coverage, American news media use polar sentiment words to create bleeding images of natural disasters, potentially counter-productive to the wisdom of dealing with the natural disaster. Identifying the sentiment words that lead to a misperception of natural disasters can help journalists adopt the wisdom that natural disasters are not a human enemy. The corpus-assisted discourse studies (CADS) reported in this article investigates the American media's issues for dramatic reporting and the polar sentiment words utilized in the framing. The corpus is built from 100 news articles reporting wildfires and storms by ten major online American news media published from January 1, 2018, through December 31, 2020. It uses AntConc to generate word-list and word-link from which it identifies the dominant issues. Subsequently, it compares the AntConc word-list with A List of Sentiment Words to reveal the tones and dramatic imaging. The findings show that the dominant issues in storm reporting are description, impact, and prediction, while wildfire reporting are cause, impact, action, and prediction. The negative polar words produce dramatic images of storm as a violent beast and wildfire as a vengeful invader. Such description is provocative to blaming natural disasters as a cause of human suffering rather than improving our behaviors to reduce the suffering. Thus, it is counter-productive to acquiring wisdom for dealing with natural disasters.

SARS-CoV-2 and HIV-1: Should HIV-1-Infected Individuals in Sub-Saharan Africa Be Considered a Priority Group for the COVID-19 Vaccines?

Since its emergence in 2019 SARS-CoV-2 has proven to have a higher level of morbidity and mortality compared to the other prevailing coronaviruses. Although initially most African countries were spared from the devastating effect of SARS-CoV-2, at present almost every country has been affected. Although no association has been established between being HIV-1-infected and being more vulnerable to contracting COVID-19, HIV-1-infected individuals have a greater risk of developing severe COVID-19 and of COVID-19 related mortality. The rapid development of the various types of COVID-19 vaccines has gone a long way in mitigating the devastating effects of the virus and has controlled its spread. However, global vaccine deployment has been uneven particularly in Africa. The emergence of SARS-CoV-2 variants, such as Beta and Delta, which seem to show some subtle resistance to the existing vaccines, suggests COVID-19 will still be a high-risk infection for years. In this review we report on the current impact of COVID-19 on HIV-1-infected individuals from an immunological perspective and attempt to make a case for prioritising COVID-19 vaccination for those living with HIV-1 in Sub-Saharan Africa (SSA) countries like Malawi as one way of minimising the impact of COVID-19 in these countries.

Research on Urban Electric Vehicle Public Charging Network Based on 5G and Big Data

Under the pressure of energy and environmental protection, we will promote the technological progress and demonstration of electric vehicles, and the construction of charging facilities will continue. Charging facilities planning and orderly charging, as two major research directions of electric vehicle infrastructure, are of great significance for the future development of electric vehicles. The optimal charging of electric vehicles can effectively improve the safe and economic operation ability of distribution network, which is of great significance to its safe operation. Therefore, this paper proposes the outsourcing test experiment and processing of urban electric vehicle public charging network based on 5G and big data. In this paper, through the analysis of the development status of urban electric vehicles, this paper proposes to optimize the charging mode of electric vehicles by combining the charging network forward and backward algorithm. In the outsourcing test experiment, the electrical safety test shows that when the current reaches 1.1-37.1kw: 5000A, when the power factor is 0.8 ∼ 0.9, when the short-circuit current impact is tolerated, the connection device will not affect the breaking operation by contact fusion welding, and the insulation protection will not be invalid. Through investigation and analysis, the satisfaction degree of electric vehicle optimization algorithm is increasing year by year. Through the analysis of the test results, the research in this paper has achieved ideal results and made a contribution to the research of urban electric vehicle public charging network.

A Study on the Current Impact on Island Tourism Development under COVID-19 Epidemic Environment and Infection Risk: A Case Study of Penghu

The present study examined the impact on island tourism development during the COVID-19 epidemic environment and infection risk by using Penghu as a case study. Using a mixed re-search methodology, 534 questionnaires were collected and analyzed using IBM SPSS Statistics 22.0 for Windows statistical software with statistical tests and t-tests. The views of scholars, experts, residents, and tourists on the questionnaire results were then compiled and finally examined by multivariate validation analysis. The results showed that different stakeholders maintained different perspectives on a number of economic, social, and environmental issues in the epidemic environment with risks of infection. Residents considered that the preservation of marine culture and the lack of resting and parking facilities for tourists are the issues that need to be improved in the development of Penghu tourism. Visitors believe that improving littering, vessel mooring space, pollution from heavy oil discharges, landscape and historic site protection, surface litter and pollution in the harbor, marine habitat, heavy oil spills, tourist litter, and threats from invasive species will help attract tourists to visit and spend money.

Social sensing of high-impact rainfall events worldwide: a benchmark comparison against manually curated impact observations

Impact-based weather forecasting and warnings create the need for reliable sources of impact data to generate and evaluate models and forecasts. Here we compare outputs from social sensing – analysis of unsolicited social media data, in this case from Twitter – against a manually curated impact database created by the Met Office. The study focuses on high-impact rainfall events across the globe between January–June 2017. Social sensing successfully identifies most high-impact rainfall events present in the manually curated database, with an overall accuracy of 95 %. Performance varies by location, with some areas of the world achieving 100 % accuracy. Performance is best for severe events and events in English-speaking countries, but good performance is also seen for less severe events and in countries speaking other languages. Social sensing detects a number of additional high-impact rainfall events that are not recorded in the Met Office database, suggesting that social sensing can usefully extend current impact data collection methods and offer more complete coverage. This work provides a novel methodology for the curation of impact data that can be used to support the evaluation of impact-based weather forecasts.