Grammatical gender systems are prevalent across many languages, and when comparing French and English the existence of this system becomes a strong distinction. There have been studies that attribute assigned grammatical gender with the ability to influence conceptualization (attributing gender attributes) of all nouns, thus affecting people's thoughts on a grand scale. We hypothesized that due to the influence of a grammatical gender system, French political discourse would have a large difference between the number of masculine and feminine nouns used. Specifically, we predicted there would be a larger ratio of feminine to masculine nouns in French political discourse than in non-political discourse when compared to English discourse. Through linguistic analysis of gendered nouns in French political writing, we found that there is a clear difference between the number of feminine versus masculine nouns, signaling a preference for a more “effeminate” language.

The authors analyze political endorsement patterns and impacts from the 2018 and 2020 midterm elections and find that such endorsements may be predictable based on the ideological and demographic factors of the endorser.

Auto-Regressive Integrated Moving Average (ARIMA) models are known for their influence and application on time series data. This statistical analysis model uses time series data to depict future trends or values: a key contributor to crime mapping algorithms. However, the models may not function to their true potential when analyzing data with many different patterns. In order to determine the potential of ARIMA models, our research will test the model on irregularities in the data. Our team hypothesizes that the ARIMA model will be able to adapt to the different irregularities in the data that do not correspond to a certain trend or pattern. Using crime theft data and an ARIMA model, we determined the results of the ARIMA model’s forecast and how the accuracy differed on different days with irregularities in crime.

In this article the authors investigate whether there is a correlation between age of a person and their outlook on global issues such as technology, politics, and environment. They find a correlation between increased age and decreased optimism. However regardless of age, they find that respondents believe certain characteristics such as technology and willingness to change are essential for improvements.

In this study, the authors investigate the demographic indicators for voter shift between the 2016 and 2020 presidential elections based on demographic data put through a K-nearest neighbors classification algorithm and Principal Component Analysis.

Quantum computers can perform computational tasks beyond the capability of classical computers, such as simulating quantum systems in materials science and chemistry. Quantum teleportation is the transfer of quantum information across distances, relying on entangled states generated by quantum computing. We sought to mitigate the error of quantum teleportation which was simulated on IBM cloud quantum computers.

In this work, the authors investigate the accuracy with which two different population growth models can predict population growth over time. They apply the Malthusian law or Logistic law to US population from 1951 until 2019. To assess how closely the growth model fits actual population data, a least-squared curve fit was applied and revealed that the Logistic law of population growth resulted in smaller sum of squared residuals. These findings are important for ensuring optimal population growth models are implemented to data as population forecasting affects a country's economic and social structure.

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.

In this study, the authors examined how Sri Lankan Americans (SLAs) view racial issues in the U.S. The main hypothesis is that SLAs, as a minority in the U.S., are supportive of the Black Lives Matter movement and its political goal, challenging the common notion that SLAs are anti-Black. The study found that a majority of SLAs believe the U.S. has systemic racism, favor BLM, and favor affirmative action. IT also found that Tamil SLAs have more favorable views of BLM and affirmative action than Sinhalese SLAs.

Despite major advances in gender equality, men still far outnumber women in science, technology, engineering and math (STEM) professions. The purpose of this project was to determine whether mindset could affect a student’s future career choices and whether this effect differed based on gender. When looking within the gender groups, 86% of females who had a growth mindset were likely to consider a “male” career, whereas only 16% of females with fixed mindset would likely to consider a “male” career. Especially for girls, cultivating a growth mindset may be a great strategy to address the problem of fewer girls picking STEM careers.