The Impact of Remote Hearing Policies on Racial Equity in Criminal Case Outcomes During the Pandemic

The criminal justice system confronted unprecedented challenges during the COVID-19 pandemic. In response, court systems nationwide quickly instituted policies to enable criminal cases to proceed while protecting public health. The shift toward criminal hearings by videoconference or teleconference has persisted. All fifty states now conduct criminal hearings remotely. Yet evidence about how remote proceedings affect case outcomes remains sparse. Using data for all arrests and criminal case dispositions that occurred in California between 2018 and mid-2021, I characterize the impact the pandemic had on arrest and case resolution rates, estimate the impact of adopting policies to permit remote hearings on conviction and sentencing outcomes, and determine which factors contributed to racial differences in outcomes. Remote hearing policies contributed to racial inequalities in outcomes, which predated the pandemic and persisted amid it.

BIDEN CALLS FOR BOOSTS TO US SCIENCE SPENDING TO STAY COMPETITIVE

In particular, the National Science Foundation (NSF) would see its budget increase by nearly 19%, and the Department of Energy's Office of Science - a big investor in the physical sciences - would see an increase of nearly 9% (see 'Biden's budget requests for science in 2024'). "The public-health system has been so underfunded for so long that the truth of the matter is, it's going to take a fair amount of money to make that right, but it's a step in the right direction," he says. Furthermore, the budget would provide $24 billion to help US communities prepare for the rising impacts of climate change, and another $7 billion to help communities that depend on oil, gas and coal extraction to tran-sition to clean energy.

To what extent does renewable energy deployment reduce pollution indicators? the moderating role of research and development expenditure: Evidence from the top three ranked countries

Climate change, pollution, drought, and rising seas impede the achievement of the seventh sustainable development goal SDG#7 (i.e. affordable and clean energies).To counter these threats, the use of Renewable Energy (RE) as an alternative to conventional energy has an important role to play in sustainable development. In this context, the purpose of our paper is to investigate the effect of RE deployment on environmental protection in China, The United States of America (USA), and Germany: the top three ranked countries in terms of RE production, according to RENEWABLE 2021 GLOBAL STATUSREPORT. To achieve this objective, the paper adopts a Panel fully modified OLS (FMOLS) method. Results declare that renewable energy significantly reduce pollution indicators;furthermore, we find that Research and development fully moderate this relationship. The findings of this study emphasize the importance of increasing spending on Research and development activities in the RE sector. In addition, the countries studied and countries around the world should pay greater attention to investment in research and development to support the long-term plan for advancing sustainable energy sources for feasible energy and economic development.

Spatio-temporal Graph Learning for Epidemic Prediction

The COVID-19 pandemic has posed great challenges to public health services, government agencies, and policymakers, raising huge social conflicts between public health and economic resilience. Policies such as reopening or closure of business activities are formulated based on scientific projections of infection risks obtained from infection dynamics models. Though most parameters in epidemic prediction service models can be set with domain knowledge of COVID-19, a key parameter, namely, human mobility, is often challenging to estimate due to complex spatio-temporal correlations and social contexts under escalating COVID-19 facilities. Moreover, how to integrate the various implicit features to accurately predict infectious cases is still an open issue. To address this challenge, we formulate the problem as a spatio-temporal network representation problem and propose STEP, a Spatio-Temporal Epidemic Prediction framework, to estimate pandemic infection risk of a city by integrating various real-world conditions (e.g., City Risk Index, climate, and medical conditions) into graph-structured data. We also employ a multi-head attention mechanism in representation learning to extract implicit features for a given city. Extensive experiments have been conducted upon the real-world dataset for 51 states (50 states and Washington, D.C.) of the USA. Experimental results show that STEP can yield more accurate pandemic infection risk estimation than baseline methods. Moreover, STEP outperforms other methods in both short-term and long-term prediction. [ABSTRACT FROM AUTHOR] Copyright of ACM Transactions on Intelligent Systems & Technology is the property of Association for Computing Machinery and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Context and Considerations for the Development of Community-Informed Health Communication Messaging to Support Equitable Uptake of COVID-19 Vaccines Among Communities of Color in Washington, DC.

BACKGROUND: Communities of color have been disproportionately impacted by COVID-19. We explored barriers and facilitators to COVID-19 vaccine uptake among African American, Latinx, and African immigrant communities in Washington, DC. METHODS: A total of 76 individuals participated in qualitative interviews and focus groups, and 208 individuals from communities of color participated in an online crowdsourcing contest. RESULTS: Findings documented a lack of sufficient, accurate information about COVID-19 vaccines and questions about the science. African American and African immigrant participants spoke about the deeply rooted historical underpinnings to their community's vaccine hesitancy, citing the prior and ongoing mistreatment of people of color by the medical community. Latinx and African immigrant participants highlighted how limited accessibility played an important role in the slow uptake of COVID-19 vaccines in their communities. Connectedness and solidarity were found to be key assets that can be drawn upon through community-driven responses to address social-structural challenges to COVID-19 related vaccine uptake. CONCLUSIONS: The historic and ongoing socio-economic context and realities of communities of color must be understood and respected to inform community-based health communication messaging to support vaccine equity for COVID-19 and other infectious diseases.

An Interview with Jay Goff

Currently, he is Vice Provost for Enrollment and Student Success at The George Washington University in Washington D.C. Goff leads GW's enrollment management division, which includes the offices of pre-college programs, undergraduate admissions, student financial assistance, the registrar, student success, graduate enrollment management, and career services. [...]that was a really nice benefit-it helped make the work environment a little less stressful and helped me know the leaders who I was relying on to drive a lot of change and fast innovation. [...]Canada has seen an increase in the numbers of international students studying here, especially over the past five years, and managed to maintain those numbers, for the most part, during the pandemic by allowing international students to study remotely in their home country while still qual ifying for their work visa in Canada upon graduation. According to The Institute of International Education,2 international enrollment fell 15 percent in fall 2020 at the more than 700 schools it surveyed.

Medical Boards: Impact of Growing Virtual Care and Need for Integrated Approach to Enhance Quality and Safety

The increased amount of virtual care during the COVID-19 pandemic has exacerbated the challenge of providing appropriate medical board oversight to ensure proper quality of care delivery and safety of patients. This is partly due to the conventional model of each state medical board (SMB) holding responsibility for medical standards and oversight only within the jurisdiction of that state board and partly due to regulatory waivers and reduced enforcement of privacy policies. Even with a revoked license in one state, significant number of physicians have continued to practice by obtaining a medical license in a different state. Individualized requests were sent to 63 medical boards with questions related to practice of telemedicine and digital health by debarred or penalized medical doctors. The responses revealed major deficiencies and the urgent need to adopt a nationwide framework and to create an anchor point to serve as the coordinator of all relevant information related to incidents of improper medical practice. The ability to cause damage to large number of patients is significantly more now. Federal and state agencies urgently need to provide more attention and funding to issues related to quality of care and patient care in the changing ecosystem that includes medical specialists at a distance and the use of evolving digital health services and products. The creation, maintenance, and use of an integrated information system at national and multinational levels is increasingly important.

"EDUCATION UNDER ARMED GUARD": AN ANALYSIS OF THE SCHOOL-TO-PRISON PIPELINE IN WASHINGTON,. D.C

The school-to-prison pipeline funnels too many students from the classroom into the criminal justice system. School policing, the criminalization of student conduct, and exclusionary discipline all contribute to this phenomenon. In 2018-19 the last full term before the COVID-19 pandemic necessitated a shift to online learning each of these practices existed to some degree in Washington, D.C. Police patrolled schools attended by a majority of the city's students, several hundred students were referred to law enforcement, and more than 6% of students were suspended. But this need not be the case. If local officials commit to the responsible elimination of school policing, limit the degree to which students face criminal consequences for school conduct, and further limit exclusionary punishments, (hey can reduce the degree to which school discipline leads to incarceration in Washington, D.C. Indeed, the city's policymakers have the opportunity to ensure that, as students continue to return to campus, they do not face the school-to-prison pipeline.

Machine learning Models to Predict COVID-19 Cases

Coronavirus can be transmitted through the air by close proximity to infected persons. Commercial aircraft are a likely way to both transmit the virus among passengers and move the virus between locations. The importance of learning about where and how coronavirus has entered the United States will help further our understanding of the disease. Air travelers can come from countries or areas with a high rate of infection and may very well be at risk of being exposed to the virus. Therefore, as they reach the United States, the virus could easily spread. On our analysis, we utilized machine learning to determine if the number of flights into the Washington DC Metro Area had an effect on the number of cases and deaths reported in the city and surrounding area. © 2022 IEEE.

NOWHERE TO GO: ANTI-ASIAN HATE CRIMES IN 1945 AND TODAY

Higuchi asserts that Iyekichi Higuchi prepared to leave the Heart Mountain camp for Japanese Americans in May 1945 to return to San Jose, California, look for a home for his wife and two at-home children, and to find a job. He had been forced to sell his 14.25-acre home in San Jose three years earlier when the federal government had forced 120,000 Japanese Americans from the West Coast because of hysteria about the alleged security threat they posed in the days following the 7 December 1941, Japanese attack on the naval base at Pearl Harbor, Hawaii. What faced those returning Japanese Americans mirrors the hate crimes now facing Americans of Asian descent who are blamed for spreading the COVID-19 virus that originally started in China to the United States. Since the pandemic took over in March, thousands of Asian Americans have been accosted in public spaces, spit on or assaulted and told to go back where they came from, even if that was not Asia at all.

Missed Opportunity: The West Baltimore Opportunity Zones Story

This paper presents a qualitative evaluation of how Opportunity Zones (OZs) have attracted capital and economic development to highly distressed neighborhoods in West Baltimore. Based on 76 interviews with community and government officials, program managers, developers, businesses, and fund managers, we assess the strengths and weaknesses of OZs in West Baltimore and Baltimore City. We find that OZs are stimulating new investment conversations and building local economic development capacity. However, we also find OZs fail at oversight and community engagement, do not spur new development, and are a missed opportunity to incentivize actors and institutions critical to revitalizing distressed neighborhoods. To spur development in distressed neighborhoods, OZs require reporting standards, the removal of non-distressed census tracts, dollars for education and infrastructure, the incorporation of Community Development Financial Institutions, and incentives for non-capital gains holding investors.

The Impact of Qualified Opportunity Zones on Existing Single-Family House Prices

Established by the Tax Cuts and Jobs Act of 2017 (TCJA),1 qualified Opportunity Zones (OZs) are a new place-based community development program that attempts to help economically challenged areas by encouraging private capital investment through the use of tax incentives. Although the program started at the beginning of 2018, implementation of the program has been slow, creating challenges for investors. The program's structure may have also inadvertently created an environment ripe for surging property prices. This unintended consequence has the potential to reduce or eliminate investor tax benefits, stimulate community gentrification, and diminish affordability for residents. Recent studies have found evidence of material price "premiums" for some commercial real estate properties located in OZs (Pierzak, 2021;Sage, Langen, and Van de Minne, 2019). Recognizing the policy's potential in driving increased investor interest in single-family home rentals, the authors of this study explore the impact of the program on existing single-family house prices and find that the community development program has led to excess home price appreciation totaling 6.8 percent from 2018 to 2020.

Pandemic Injustice: Irish Immigrant, Enslaved African American, and Choctaw Experiences with Cholera in 1832

Cholera victims appeared healthy one moment but then quickly experienced agonizing symptoms: vomiting and voluminous diarrhea filled with blood plasma and flakes of the small intestine that gave the stool a characteristic rice-water appearance. No one understood, in those days before germ theory, that the bacteria Vibrio cholerae caused the illness, that one contracted it by consuming sewage-contaminated water or food, and that the bacteria released one of nature's deadliest toxins into the small intestine. Mild cases resulted when an individual ingested relatively few bacteria and when an individual's relatively high level of stomach acid killed significant quantities of the germs before they reached the small intestine. Pandemics-whether influenza in 1918 or COVID-19 in 2020-have generally had higher case fatality rates in the United States among recent immigrants, African Americans, and Indigenous populations.6 As has happened in the more recent past and as is happening today, a pandemic and injustice marched in lockstep through the American South in 1832.7 In midsummer of that deadly year, cholera penetrated the American South.

Predicting Demand for Shared E-Scooter Using Community Structure and Deep Learning Method

The shared e-scooter is a popular and user-convenient mode of transportation, owing to the free-floating manner of its service. The free-floating service has the advantage of offering pick-up and drop-off anywhere, but has the disadvantage of being unavailable at the desired time and place because it is spread across the service area. To improve the level of service, relocation strategies for shared e-scooters are needed, and it is important to predict the demand for their use within a given area. Therefore, this study aimed to develop a demand prediction model for the use of shared e-scooters. The temporal scope was selected as October 2020, when the demand for e-scooter use was the highest in 2020, and the spatial scope was selected as Seocho and Gangnam, where shared e-scooter services were first introduced and most frequently used in Seoul, Korea. The spatial unit for the analysis was set as a 200 m square grid, and the hourly demand for each grid was aggregated based on e-scooter trip data. Prior to predicting the demand, the spatial area was clustered into five communities using the community structure method. The demand prediction model was developed based on long short-term memory (LSTM) and the prediction results according to the activation function were compared. As a result, the model employing the exponential linear unit (ELU) and the hyperbolic tangent (tanh) as the activation function produced good predictions regarding peak time demands and off-peak demands, respectively. This study presents a methodology for the efficient analysis of the wider spatial area of e-scooters.

WASHINGTON DC REGIONAL ECMO OUTCOMES IN COVID-19

B Introduction: b COVID-19 mortality has disproportionately impacted racial and ethnic minorities in the United States. This disparity is particularly notable in Washington D.C. where African Americans make up 44% of the population, but 51% of COVID-19 infections and 75% of COVID-19 mortalities. To future explore socioeconomic status (SES) as a corollary to ECMO cannulation, we merged patient zip codes with zip code median household income from the American Community Survey. [Extracted from the article] Copyright of Critical Care Medicine is the property of Lippincott Williams & Wilkins and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Dynamic Panel Data Modeling and Surveillance of COVID-19 in Metropolitan Areas in the United States: Longitudinal Trend Analysis.

BACKGROUND: The COVID-19 pandemic has had profound and differential impacts on metropolitan areas across the United States and around the world. Within the United States, metropolitan areas that were hit earliest with the pandemic and reacted with scientifically based health policy were able to contain the virus by late spring. For other areas that kept businesses open, the first wave in the United States hit in mid-summer. As the weather turns colder, universities resume classes, and people tire of lockdowns, a second wave is ascending in both metropolitan and rural areas. It becomes more obvious that additional SARS-CoV-2 surveillance is needed at the local level to track recent shifts in the pandemic, rates of increase, and persistence. OBJECTIVE: The goal of this study is to provide advanced surveillance metrics for COVID-19 transmission that account for speed, acceleration, jerk and persistence, and weekly shifts, to better understand and manage risk in metropolitan areas. Existing surveillance measures coupled with our dynamic metrics of transmission will inform health policy to control the COVID-19 pandemic until, and after, an effective vaccine is developed. Here, we provide values for novel indicators to measure COVID-19 transmission at the metropolitan area level. METHODS: Using a longitudinal trend analysis study design, we extracted 260 days of COVID-19 data from public health registries. We used an empirical difference equation to measure the daily number of cases in the 25 largest US metropolitan areas as a function of the prior number of cases and weekly shift variables based on a dynamic panel data model that was estimated using the generalized method of moments approach by implementing the Arellano-Bond estimator in R. RESULTS: Minneapolis and Chicago have the greatest average number of daily new positive results per standardized 100,000 population (which we refer to as speed). Extreme behavior in Minneapolis showed an increase in speed from 17 to 30 (67%) in 1 week. The jerk and acceleration calculated for these areas also showed extreme behavior. The dynamic panel data model shows that Minneapolis, Chicago, and Detroit have the largest persistence effects, meaning that new cases pertaining to a specific week are statistically attributable to new cases from the prior week. CONCLUSIONS: Three of the metropolitan areas with historically early and harsh winters have the highest persistence effects out of the top 25 most populous metropolitan areas in the United States at the beginning of their cold weather season. With these persistence effects, and with indoor activities becoming more popular as the weather gets colder, stringent COVID-19 regulations will be more important than ever to flatten the second wave of the pandemic. As colder weather grips more of the nation, southern metropolitan areas may also see large spikes in the number of cases.

The Impact of COVID-19 on CO2 Emissions in the Los Angeles and Washington DC/Baltimore Metropolitan Areas.

Responses to COVID-19 have resulted in unintended reductions of city-scale carbon dioxide (CO2) emissions. Here, we detect and estimate decreases in CO2 emissions in Los Angeles and Washington DC/Baltimore during March and April 2020. We present three lines of evidence using methods that have increasing model dependency, including an inverse model to estimate relative emissions changes in 2020 compared to 2018 and 2019. The March decrease (25%) in Washington DC/Baltimore is largely supported by a drop in natural gas consumption associated with a warm spring whereas the decrease in April (33%) correlates with changes in gasoline fuel sales. In contrast, only a fraction of the March (17%) and April (34%) reduction in Los Angeles is explained by traffic declines. Methods and measurements used herein highlight the advantages of atmospheric CO2 observations for providing timely insights into rapidly changing emissions patterns that can empower cities to course-correct CO2 reduction activities efficiently.