Many U.S. schools closed nationwide in March 2020 to prevent the spread of COVID-19. School closures and online-only instruction have negatively affected certain students, with studies showing adverse effects of the pandemic on mental health. However, little is known about other experiences such as economic and food insecurity and abuse by a parent, as well as risk behaviors such as alcohol and drug use among youths across the United States during the pandemic. To address this gap, CDC developed the one-time, online Adolescent Behaviors and Experiences Survey (ABES), which was conducted during January–June 2021 to assess student behaviors and experiences during the COVID-19 pandemic among high school students, including unintentional injury, violence, tobacco product use, sexual behaviors, and dietary behaviors. This overview report of the ABES MMWR Supplement describes the ABES methodology, including the student questionnaire and administration, sampling, data collection, weighting, and analysis. ABES used a stratified, three-stage cluster probability-based sampling approach to obtain a nationally representative sample of students in grades 9–12 attending public and private schools. Teachers of selected classes provided students with access to the anonymous online survey while following local consent procedures. Data were collected using a 110-item questionnaire during January–June 2021 in 128 schools. A total of 7,998 students submitted surveys, and 7,705 of these surveys had valid data (i.e., ≥20 questions answered). The school response rate was 38%, the student response rate was 48%, and the overall response rate was 18%. Information on mode of instruction and school-provided equipment was also collected from all sampled schools. This overview report provides student- and school-level characteristics obtained from descriptive analyses, and the other reports in the ABES MMWR Supplement include information on substance use, mental health and suicidality, perceived racism, and disruptions to student life among high school students. Findings from ABES during the COVID-19 pandemic can help guide parents, teachers, school administrators, community leaders, clinicians, and public health officials in decision-making for student support and school health programs.

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Race cars are routinely driven to the edge of their handling limits in dynamic scenarios well above 200mph. Similar challenges are posed in autonomous racing, where a software stack, instead of a human driver, interacts within a multi-agent environment. For an Autonomous Racing Vehicle (ARV), operating at the edge of handling limits and acting safely in these dynamic environments is still an unsolved problem. In this paper, we present a baseline controls stack for an ARV capable of operating safely up to 140mph. Additionally, limitations in the current approach are discussed to highlight the need for improved dynamics modeling and learning.

For robotics systems to be used in high risk, real-world situations, they have to be quickly deployable and robust to environmental changes, under-performing hardware, and mission subtask failures. Robots are often designed to consider a single sequence of mission events, with complex algorithms lowering individual subtask failure rates under some critical constraints. Our approach is to leverage common techniques in vision and control and encode robustness into mission structure through outcome monitoring and recovery strategies, aided by a system infrastructure that allows for quick mission deployments under tight time constraints and no central communication. We also detail lessons in rapid field robotics development and testing. Systems were developed and evaluated through real-robot experiments at an outdoor test site in Pittsburgh, Pennsylvania, USA, as well as in the 2020 Mohamed Bin Zayed International Robotics Challenge. All competition trials were completed in fully autonomous mode without RTK-GPS. Our system led to 4th place in Challenge 2 and 7th place in the Grand Challenge, and achievements like popping five balloons (Challenge 1), successfully picking and placing a block (Challenge 2), and dispensing the most water autonomously with a UAV of all teams onto an outdoor, real fire (Challenge 3).