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In this study, the authors survey students and adults to better understand their basic financial knowledge and money saving skills to measure the extent of knowledge in each group and make comparisons between.

Diabetes is a serious worldwide epidemic that affects a growing portion of the population. While the most common method for testing blood glucose levels involves finger pricking, it is painful and inconvenient for patients. The authors test a non-invasive method to measure glucose levels from diabetic patients, and investigate whether the method is clinically accurate and universally applicable.

Previous studies have found that how a food item is labeled may influence people's liking of it. This study used a cookie taste test to investigate whether people's liking of a dessert item would be swayed by the use of different labels.

Pay is a widely debated factor in workplace motivation, influencing both incentives and job satisfaction. This work analyzed employee reviews across various industries to examine the relationship between pay importance and job satisfaction. Findings suggest that job satisfaction decreases as the importance of pay increases, particularly in construction, food, and finance industries, as well as among entry-level and experienced workers, though the results were not statistically significant.

The authors train a neural network to detect text-based emotions including joy, sadness, anger, fear, love, and surprise.

Pulmonary diseases like lung cancer and valley fever pose serious health challenges, making accurate and rapid diagnostics essential. This study developed a MATLAB-based software tool that uses computer vision techniques to differentiate between these diseases by analyzing features of lung nodules in CT scans, achieving higher precision than traditional methods.

The authors looked at using machine learning to identify skills needed to apply for certain jobs, specifically looking at different techniques to parse apart the text. They found that Bidirectional Encoder Representation of Transforms (BERT) performed best.

Skin cancer is a common and potentially deadly form of cancer. This study’s purpose was to develop an automated approach for early detection for skin cancer. We hypothesized that convolutional neural network-based models using transfer learning could accurately differentiate between benign and malignant moles using natural images of human skin.

Plastic pollution in the ocean is a major global concern. Remotely Operated Vehicles (ROVs) have promise for removing debris from the ocean, but more research is needed to achieve full effectiveness of the ROV technology. Wahlig and Gonzales tackle this issue by developing a deep learning model to distinguish trash from the environment in ROV images.

Here the authors sought to understand the effects of different variables that may be tied to pollution and climate change on genetic variation of Pacific white-sided dolphins, a species that is currently threatened by water pollution. Based on environmental data collected alongside a genetic distance matrix, they found that ocean currents had the most significant impact on the genetic diversity of Pacific white-sided dolphins along the Japanese coast.