Sadly, around 800,000 people die by suicide worldwide each year. Dong and Pearce analyze health survey data to identify associations between suicidal ideation and relevant variables, such as sleep quality, hopelessness, and anxious behavior.

In this study, the authors tested the ability and accuracy of a neural net to identify patterns in complex number matrices.

With an increasing older adult population and rapid advancements in technology, it is important that senior citizens learn to use new technologies to remain active in society. A variety of factors on learning were investigated through surveys of senior citizens. Older adults preferred an interactive lesson style, which also seemed to help them retain more course material.

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.

The authors investigate the ability of machine learning models to developing new drug-like molecules by learning desired chemical properties versus simply generating molecules that similar to those in the training set.

Effective treatment of depression requires early detection. Depressive symptoms overlap with olfactory regions, which led to several studies of the correlation between sense of smell and depression. The alarming rise of depression, its related crimes, suicides, and lack of inexpensive, quick tools in detecting early depression — this study aims in demonstrating decreased olfaction and depression correlation. Forty-two subjects (ages 13-83) underwent POC-MON (Pocket Lemon) assessment — an oven-dried lemon peel sniff test, subjected to distance measurement when odor first detected (threshold) and completed Patient Health Questionnaires (PHQ-9). POC-MON and PHQ-9 scores yielded a correlation of 20% and 18% for the right and left nostrils, respectively. Among male (n=17) subjects, the average distance of POC-MON and PHQ-9 scores produced a correlation of 14% and 16% for the right and left nostrils, respectively. Females (n=25) demonstrated a correlation of 28% and 21% for the right and left nostrils, respectively. These results suggest the correlation between olfaction and depression in diagnosing its early-stage, using a quick, inexpensive, and patient-friendly tool — POC-MON.

In this study, high school students and teachers responded to a survey consisting of Likert-type scale, multiple-choice, and open-ended questions regarding various aspects of remote instruction. After analyzing the data collected, they found that remote learning impacted high school students academically and socially. Students took longer to complete assignments, and both students and teachers felt that students do not learn as much in remote learning compared to in-person instruction. However, most high school students demonstrated a comprehensive understanding of the topics, and an overall negative impact on students' grades was not detected.

In this study, Donnellan and colleagues investigated how environmental pollution may be affecting honey samples from Chicago apiaries. They found no significant correlation between heavy metal concentration in honey to distance from local industries, suggesting a minimal effect of proximity to industrial pollution on honey contamination.

Supernovas are powerful explosions that result from gravitational collapse of a massive star. Using photometric analysis Arora et al. set out to investigate whether 2020pni (located in galaxy UGC 9684) was a supernova. They were ultimately able to identify 2020pni as a Type II-L supernova and determine it's distance from earth.

The purpose of the study was to determine whether graph-based machine learning techniques, which have increased prevalence in the last few years, can accurately classify data into one of many clusters, while requiring less labeled training data and parameter tuning as opposed to traditional machine learning algorithms. The results determined that the accuracy of graph-based and traditional classification algorithms depends directly upon the number of features of each dataset, the number of classes in each dataset, and the amount of labeled training data used.