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.

In this study, the authors investigate the antimicrobial effects of berberine and berberine analogs. Berberine is extracted from plants and is a naturally occurring alkaloid, and is also excited photochemically. Using three different assays, the authors tested whether these compounds would inhibit bacterial growth. They found that these compounds were antibacterial and even more so when used with photoirradiation. This study has important antibacterial implications.

Using facial recognition as a use-case scenario, we attempt to identify sources of bias in a model developed using transfer learning. To achieve this task, we developed a model based on a pre-trained facial recognition model, and scrutinized the accuracy of the model’s image classification against factors such as age, gender, and race to observe whether or not the model performed better on some demographic groups than others. By identifying the bias and finding potential sources of bias, his work contributes a unique technical perspective from the view of a small scale developer to emerging discussions of accountability and transparency in AI.

In this report, Bhardwaj and Sharma tested whether placing specific plants indoors can reduce levels of indoor air pollution that can lead to lung-related illnesses. Using machine learning, they show that plants improved overall indoor air quality and reduced levels of particulate matter. They suggest that plant-based interventions coupled with sensors may be a useful long-term solution to reducing and maintaining indoor air pollution.

In this study the author undertakes a careful characterization of a special type of chemical reaction, called an oscillating Belousov-Zhabotinsky (or B-Z) reaction, which has a number of existing applications in biomedical engineering as well as the potential to be useful in future developments in other fields of science and engineering. Specifically, she uses experimental measurements in combination with computational analysis to investigate whether the reaction is cohesive – that is, whether the oscillations between chemical states will remain consistent or change over time as the reaction progresses. Her results indicate that the reaction is not cohesive, providing an important foundation for the development of future technologies using B-Z reactions.

The authors compare neuroimaging datasets to identify potential new biomarkers for earlier detection of Alzheimer's disease.

In this study, the authors use aerosol optical depth data to determine if aerosol levels were lower in major metropolitan areas around the world during the COVID-19 pandemic.

The authors investigate how well AI-detection machine learning models can detect real versus AI-generated art across different art styles.

Studying other galaxies can help us understand the origins of the universe. Here, the authors study a type of galaxies known as Green Peas gaining insights that could help inform our understanding of Lyman alpha emitters, one of the first types of galaxies that existed in the early universe.

In this study, the authors investigate the relationship between iron/hydrogen ratio [Fe/H] of a type of variable stars commonly used as reference points RR Lyrae stars and their light curves to see if one can determine the composition of these stars solely by measuring their light curve characteristics.