In this study, the authors investigate the effects of the COVID-19 pandemic on South Korean international school students' anxiety, well being and their learning habits.

While 75% of waste in the United States is stated to be recyclable, only about 34% truly is. This project takes a stance to combat the pillars of mismanaged waste through a modern means of convenience: the TracedWaste app. The purpose of this study was to identify how individuals' waste disposal habits improved and knowledge increased (i.e. correctly disposing of waste, understanding negative incorrect waste disposal) due to their use of an informational waste management app as measured by a survey using a 1-5 Likert Scale. The results showed that the TracedWaste app helped conserve abundant resources such as energy and wood, decrease carbon emissions, and minimize financial toll all through reducing individual impact.

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.

The authors created a board game to teach young children about healthy eating habits to see whether an interactive and family-oriented method would be effective at introducing and maintaining a love for fruits and veggies. Results showed that children developed a liking for fruits and vegetables, and none regressed. Half maintained their level of enjoyment for fruits and vegetables during the research period, while the other half had a positive increase. The results show that a simple interactive game can shape how young children relate to food and encourage them to maintain healthy habits.

Palermo et al. examined the effect of group size on drinking habits of college and high school students. The authors found that both high school and college students tended to consume the most alcohol in group sizes of 4 or more, independent of how frequently they drink. They also found that the proportion of college students that drink is nearly twice the proportion of high school students that drink. This study supports previous findings that underage drinking happens in large groups and suggests that effective intervention in underage drinking would be at the group level.

Life requires many things, including a hospitable temperature, elements, and energy. Here the authors utilize Newton's laws of physics and information relating a star's luminosity and temperature to determine the minimum and maximum masses and luminosities of planets and stars that would support life as we know it. This work can be used to determine the likelihood of a planet being able to support life based on attributes we can measure from here on Earth.

In this article, Choi and Rossitto investigated the limitations of virtual learning by examining in-person dance learning compared to virtual dance learning while wearing EEG headsets. They found that in-person learners outperformed virtual learners and that virtual learners had higher mirror neuron activity as assessed by Mu rhythm power.

The authors explore how similar exoplanets are to Earth and whether they could be inhabited by humans and other living organisms.

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.

The application of machine learning techniques has facilitated the automatic annotation of behavior in video sequences, offering a promising approach for ethological studies by reducing the manual effort required for annotating each video frame. Nevertheless, before solely relying on machine-generated annotations, it is essential to evaluate the accuracy of these annotations to ensure their reliability and applicability. While it is conventionally accepted that there cannot be a perfect annotation, the degree of error associated with machine-generated annotations should be commensurate with the error between different human annotators. We hypothesized that machine learning supervised with adequate human annotations would be able to accurately predict body parts from video sequences. Here, we conducted a comparative analysis of the quality of annotations generated by humans and machines for the body parts of sheep during treadmill walking. For human annotation, two annotators manually labeled six body parts of sheep in 300 frames. To generate machine annotations, we employed the state-of-the-art pose-estimating library, DeepLabCut, which was trained using the frames annotated by human annotators. As expected, the human annotations demonstrated high consistency between annotators. Notably, the machine learning algorithm also generated accurate predictions, with errors comparable to those between humans. We also observed that abnormal annotations with a high error could be revised by introducing Kalman Filtering, which interpolates the trajectory of body parts over the time series, enhancing robustness. Our results suggest that conventional transfer learning methods can generate behavior annotations as accurate as those made by humans, presenting great potential for further research.