Over the last few decades, childhood stunting has persisted as a major global challenge. This study hypothesized that TPTO (Tree-based Pipeline Optimization Tool), an AutoML (automated machine learning) tool, would outperform all pre-existing machine learning models and reveal the positive impact of economic prosperity, strong familial traits, and resource attainability on reducing stunting risk. Feature correlation plots revealed that maternal height, wealth indicators, and parental education were universally important features for determining stunting outcomes approximately two years after birth. These results help inform future research by highlighting how demographic, familial, and socio-economic conditions influence stunting and providing medical professionals with a deployable risk assessment tool for predicting childhood stunting.

Climate records indicate that there has been a trend of decreasing annual snowfall totals throughout the United States during the peak winter season. However, New Jersey has seen a significant increase in snowfall over the past 126 years of recorded observations. The authors hypothesize that although annual snowfall has remained the same on average, the frequencies of major and minor snowfall events have noticeably increased. They found that there was no significant evidence for an increase in the frequency of minor events (1.1-inch to 4.0-inch events), but there was evidence for an increase in the frequency of major events (4.1+ inch events). The results imply that a warming climate might be opening up opportunities for more snowfall.

Regenerative medicine has become a mainstay in recent times, and employing stem cells to treat several degenerative, inflammatory conditions has resulted in very promising outcomes. These forms of cell-based therapies are novel approaches to existing treatment modalities. In this study, the authors compared the concentrations of the cytokines PDGF, IL-8, and VEGF between conditioned and spent media of mesenchymal stem cells (MSCs) to evaluate their potential therapeutic properties for wound healing in inflammatory conditions. They hypothesized that conditioned media contains higher concentrations of wound healing cytokines compared to spent media. The authors found that while IL-8 and VEGF were present in highest concentrations in conditioned media, PDGF was present in maximal amounts in spent media.

Recurrent neural networks (RNNs) are useful for text generation since they can generate outputs in the context of previous ones. Baroque music and language are similar, as every word or note exists in context with others, and they both follow strict rules. The authors hypothesized that if we represent music in a text format, an RNN designed to generate language could train on it and create music structurally similar to Bach’s. They found that the music generated by our RNN shared a similar structure with Bach’s music in the input dataset, while Bachbot’s outputs are significantly different from this experiment’s outputs and thus are less similar to Bach’s repertoire compared to our algorithm.

Every year, more than 30% of food products go to waste. This is approximately 1.3 billion tons of food, which is equivalent to 1.3 trillion U.S. dollars. While conventional solid waste treatments and fertilization of food waste are common, citrus fruit peels require secondary applications and advanced disposal management due to their low pH values and high antimicrobial characteristics. Since citrus fruits are well-known sources of vitamin C and antioxidants, we hypothesized that their peels also contain high amounts of vitamin C and antioxidants. In our study, five common citrus peels including grapefruit, lemon, lime, orange, and tangerine, were used to determine the amounts of vitamin C and total soluble antioxidants.

Enzyme chemotaxis is a thermodynamic phenomenon in which enzymes move along a substrate concentration gradient towards regions with higher substrate concentrations and can be used to steer nanovehicles towards targets along natural substrate concentrations. In patients with Alzheimer’s disease, a gradient of tau protein forms in the bloodstream. Tau protein is a substrate of the enzyme CDK5, which catalyzes the phosphorylation of tau protein and can travel using chemotaxis along tau protein gradients to increasing concentrations of tau and amyloid-beta proteins. The authors hypothesized that CDK5 would be able to overcome these barriers of Brownian motion and developed a quantitative model using Michaelis-Menten kinetics to define the necessary parameters to confirm and characterize CDK5’s chemotactic behavior to establish its utility in drug delivery and other applications.

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.

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.

For public health, drinking water should be free of bacterial contamination. The objective of this research is to identify the fate of bacteria if drinking water becomes contaminated and inform consumers on which water type enables the least bacteria to survive. We hypothesized that bottled mineral water would provide the most sufficient conditions for E. coli to survive. We found that if water becomes contaminated, the conditions offered by the three water types at room temperature allow E. coli to survive up to three days. At 72 hours, the bottled spring water had the highest average colony forming units (CFUs), with tap and mineral water CFU values statistically lower than spring water but not significantly different from each other. The findings of this research highlight the need of implementing accessible quality drinking water for the underserved population and for the regulation of water sources.

Collisions of heavy ions, such as muons result in jets and noise. In high-energy particle physics, researchers use jets as crucial event-shaped observable objects to determine the properties of a collision. However, many ionic collisions result in large amounts of energy lost as noise, thus reducing the efficiency of collisions with heavy ions. The purpose of our study is to analyze the relationships between properties of muons in a dimuon collision to optimize conditions of dimuon collisions and minimize the noise lost. We used principles of Newtonian mechanics at the particle level, allowing us to further analyze different models. We used simple Python algorithms as well as linear regression models with tools such as sci-kit Learn, NumPy, and Pandas to help analyze our results. We hypothesized that since the invariant mass, the energy, and the resultant momentum vector are correlated with noise, if we constrain these inputs optimally, there will be scenarios in which the noise of the heavy-ion collision is minimized.