Social media is widely used and easily accessible for adolescents, it has the potential to increase STEM (Science, Technology, Engineering, and Math) identity in girls. We aimed to investigate the effects of exposure to counter-stereotypical portrayals of women in STEM on social media on the STEM identity of adolescent girls. The study concluded that social media alone may not be an effective tool to increase STEM identity in girls. Social media can still be used as a complementary tool to support and encourage women in STEM, but it should not be relied upon solely to address the gender disparity in STEM fields.

Reimagize, a role-playing with decision-making, was conjured, implementing social psychological concepts like counter-stereotyping and perspective-taking. As the game works implicitly to influence body image, it even counters image issues beyond personal body dissatisfaction. This study explored whether a digital role-playing card game, incorporating some of the most common prejudices of body image (like size prejudice, prejudices from the media, etc.) as identified by a digital survey/questionnaire completed by Indian girls aged 11-21, could counter these issues and reduce personal body dissatisfaction.

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.

Molecular dynamics (MD) simulations are a great tool to model and study complex biological systems. In this paper, the authors use MD simulations to construct and simulate a model of the periplasmic space, the peptidoglycan layer and its associated proteins, in an Escherichia coli cell.

The objective of this project was to test various materials to determine which ones collect the most atmospheric water when exposed to the same environmental factors. The experiment observed the effect of weather conditions, a material’s surface area and hydrophilicity on atmospheric water collection. The initial hypothesis was that hydrophobic materials with the greatest surface area would collect the most water. The materials were placed in the same outside location each night for twelve trials. The following day, the materials were weighed to see how much water each had collected. On average, ribbed plastic collected 10.8 mL of water per trial, which was over 20% more than any other material. This result partially supported the hypothesis because although hydrophobic materials collected more water, surface area did not have a significant effect on water collection.

While remarkable in its ability to mirror human cognition, machine learning and its associated algorithms often require extensive data to prove effective in completing tasks. However, data is not always plentiful, with unpredictable events occurring throughout our daily lives that require flexibility by artificial intelligence utilized in technology such as personal assistants and self-driving vehicles. Driven by the need for AI to complete tasks without extensive training, the researchers in this article use fluid intelligence assessments to develop an algorithm capable of generalization and abstraction. By forgoing prioritization on skill-based training, this article demonstrates the potential of focusing on a more generalized cognitive ability for artificial intelligence, proving more flexible and thus human-like in solving unique tasks than skill-focused algorithms.

The authors looked at differences in water quality between Chinatown and Bayside. They wanted to look at the racial and economic demographics of each region and how that correlated to access to clean drinking water. Ultimately they did not find any significant differences in water quality, but identified important future directions for this work.

The sequence of nitrogenous bases that make up the DNA of organisms can contain hidden mathematical sequences. Here the authors used BioPython, a programming tool, to find an organism that displays Gijswijt’s Sequence in its genome. In this manner they found that the common carp best displays Gijswijt’s Sequence in its genome.

In this study, the authors investigate whether the COVID-19 pandemic is affecting the mental health of teens. Using data from a study done in Islamabad, Pakistan, the authors find that many teens between the ages of 13 and 19 show signs of mental illness. This study reports important data regarding the mental health of youth and points toward an increased need to address this topic during the pandemic.

Here, seeking to better understand the control algorithms used in autonomous vehicles, the authors compared the Stanley and pure pursuit control algorithms along with a new version of each. Unexpectedly, they found that no control algorithm offered optimal performance, but rather resulted in tradeoffs between the various ideal results.