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Cross country is a popular sport in the U.S. Both athletes and coaches are interested in the factors that make runners successful. In this study, the authors explore the relationship between runners' physical attributes and their race performance.

In this paper, we measured the privacy budgets and utilities of different differentially private mechanisms combined with different machine learning models that forecast traffic congestion at future timestamps. We expected the ANNs combined with the Staircase mechanism to perform the best with every value in the privacy budget range, especially with the medium high values of the privacy budget. In this study, we used the Autoregressive Integrated Moving Average (ARIMA) and neural network models to forecast and then added differentially private Laplacian, Gaussian, and Staircase noise to our datasets. We tested two real traffic congestion datasets, experimented with the different models, and examined their utility for different privacy budgets. We found that a favorable combination for this application was neural networks with the Staircase mechanism. Our findings identify the optimal models when dealing with tricky time series forecasting and can be used in non-traffic applications like disease tracking and population growth.

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.

Reducing paint drying time is an important step in improving production efficiency and reducing costs. The authors hypothesized that decreased humidity would lead to faster drying, ultraviolet (UV) light exposure would not affect the paint colors differently, white light exposure would allow for longer wavelength colors to dry at a faster rate than shorter wavelength colors, and substrates with higher roughness would dry slower. Experiments showed that trials under high humidity dried slightly faster than trials under low humidity, contrary to the hypothesis. Overall, the paint drying process is very much dependent on its surrounding environment, and optimizing the drying process requires a thorough understanding of the environmental factors and their interactive effects with the paint constituents.

Although there has been great progress in the field of Natural language processing (NLP) over the last few years, particularly with the development of attention-based models, less research has contributed towards modeling keystroke log data. State of the art methods handle textual data directly and while this has produced excellent results, the time complexity and resource usage are quite high for such methods. Additionally, these methods fail to incorporate the actual writing process when assessing text and instead solely focus on the content. Therefore, we proposed a framework for modeling textual data using keystroke-based features. Such methods pay attention to how a document or response was written, rather than the final text that was produced. These features are vastly different from the kind of features extracted from raw text but reveal information that is otherwise hidden. We hypothesized that pairing efficient machine learning techniques with keystroke log information should produce results comparable to transformer techniques, models which pay more or less attention to the different components of a text sequence in a far quicker time. Transformer-based methods dominate the field of NLP currently due to the strong understanding they display of natural language. We showed that models trained on keystroke log data are capable of effectively evaluating the quality of writing and do it in a significantly shorter amount of time compared to traditional methods. This is significant as it provides a necessary fast and cheap alternative to increasingly larger and slower LLMs.

In this article the authors looked at the effect of spending time with a therapy dog before and after stressful events. They found that interacting with a therapy before a stressful event showed more significant reduction in signs of stress compared to interacting with a therapy dog after stressful events have already occurred.

In this study, the authors investigated the time-dependent cytokine secretion ability of phyto-hemagglutinin (PHA)-activated T cells derived from human peripheral (PB) and cord blood (CB). They hypothesized that the anti-inflammatory cytokine, IL-10, and pro-inflammatory cytokine, TNFα, levels would be higher in PHA-activated T cells obtained from PB as compared to the levels obtained from CB and would decrease over time. Upon PHA-activation, the IL-10 levels were relatively high while the TNFα levels decreased, making these findings applicable in therapeutic treatments e.g., rheumatoid arthritis, psoriasis, and organ transplantation.

Antibiotics are used to treat dangerous diseases. Over time, however, bacteria are becoming resistant to antibiotics - which poses a threat to humans and animals alike. In this paper, the authors examine how E. coli gains resistance to the antibiotic amoxicillin.

The challenge of accurately identifying shark species is crucial for biodiversity monitoring but is often hindered by time-consuming and labor-intensive manual methods. To address this, SharkNet, a CNN model based on AlexNet, achieved 93% accuracy in classifying shark species using a limited dataset of 1,400 images across 14 species. SharkNet offers a more efficient and reliable solution for marine biologists and conservationists in species identification and environmental monitoring.

Gun violence has been a serious issue in the South Side of Chicago for a long time. To intervene, regulators have passed legislation they hoped to curb -if not completely eradicate- the issue. However, there is little analysis done on how effective the various laws have been at reducing gun violence. Here the authors explore the association between firearm purchase laws passed between 1993-2018 and the incidence of gun homicide in Chicago's South Side. Their analysis suggests that some laws have been more effective than others, while some might have exacerbated the issue. However, they do not consider other contributing factors, which makes it difficult to prove causation without further investigation.