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In this study, the authors investigate the effect of remote learning (due to the COVID-19 pandemic) on sleeping habits amongst teenagers in Ohio. Using survey results, sleep habits and attitudes toward school were assessed before and after the COVID-19 pandemic.

In this study, the authors survey middle and high school students in different states in the U.S. to evaluate stress levels, learning experiences, and activity levels during the COVID-19 pandemic.

In this study, high school students and teachers responded to a survey consisting of Likert-type scale, multiple-choice, and open-ended questions regarding various aspects of remote instruction. After analyzing the data collected, they found that remote learning impacted high school students academically and socially. Students took longer to complete assignments, and both students and teachers felt that students do not learn as much in remote learning compared to in-person instruction. However, most high school students demonstrated a comprehensive understanding of the topics, and an overall negative impact on students' grades was not detected.

With the COVID-19 pandemic necessitating the transition to remote learning, disruption to daily school routine has impacted educational experiences on a global scale. As a result, it has potentially worsened reading achievement gaps typically exacerbated by long summer months. To address literacy skill retention and pandemic-induced social isolation, the non-profit organization ByKids4Kids has created a reading program, “Kindles4Covid Virtual Reading Buddies Program,” to instill a structure for youth to read together and connect with the convenience of Amazon Kindle devices. In this article, the authors determine the efficacy of their invaluable program by assessing changes in reading frequency and self-reported connectedness among program participants.

In this study, the impact of shutting down schools on the emotional aspects of high school students was analyzed using survey responses.

Here, seeking to identify an optimal method to classify tree species through remote sensing, the authors used a few machine learning algorithms to classify forest tree species through multispectral satellite imagery. They found the Random Forest algorithm to most accurately classify tree species, with the potential to improve model training and inference based on the inclusion of other tree properties.

Plastic pollution in the ocean is a major global concern. Remotely Operated Vehicles (ROVs) have promise for removing debris from the ocean, but more research is needed to achieve full effectiveness of the ROV technology. Wahlig and Gonzales tackle this issue by developing a deep learning model to distinguish trash from the environment in ROV images.

A bottleneck in deleting algal blooms is that current data section is manual and is reactionary to an existing algal bloom. These authors made a custom-designed Seek and Destroy Algal Mitigation System (SDAMS) that detects harmful algal blooms at earlier time points with astonishing accuracy, and can instantaneously suppress the pre-bloom algal population.

The authors test different machine learning algorithms to remove background noise from audio to help people with hearing loss differentiate between important sounds and distracting noise.

Intelligent vehicles utilize a combination of video-enabled object detection and radar data to traverse safely through surrounding environments. However, since the most momentary missteps in these systems can cause devastating collisions, the margin of error in the software for these systems is small. In this paper, we hypothesized that a novel object detection system that improves detection accuracy and speed of detection during adverse weather conditions would outperform industry alternatives in an average comparison.