Despite the prevalence of PD, diagnosing PD is expensive, requires specialized testing, and is often inaccurate. Moreover, diagnosis is often made late in the disease course when treatments are less effective. Using existing voice data from patients with PD and healthy controls, the authors created and trained two different algorithms: one using logistic regression and another employing an artificial neural network (ANN).

Stevens Creek, which flows through Santa Clara County in California, provides a crucial habitat for federally designated threatened steelhead trout, with a portion of the trout’s diet being dependent on the presence and abundance of macroinvertebrates that inhabit the creek. In this article, the authors investigate how the water chemistry within the creek was associated with the abundance and diversity of macroinvertebrates, and subsequently the creek’s health. They conduct qualitative analysis of macroinvertebrates and water quality to obtain a general understanding of the health of Stevens Creek.

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.

The phenomenon of dying trees and plants in areas affected by acid rain has become increasingly problematic in recent times. Is there any method to efficiently utilize the rainwater and reduce the harmfulness of acid rain or make it beneficial to plants? This study aimed to investigate the potential of neutralizing acid rainwater infiltrating the soil to increase soil pH, produce beneficial salts for plants, and support better plant growth. To test this hypothesis, precipitation samples were collected from six states in the U.S. in 2022, and the pH of the acid rain was measured to obtain a representative pH value for the country. Experiments were then conducted to simulate the neutralization of acid rain and the subsequent change in soil pH levels. To evaluate the effectiveness and feasibility of this method, cat grass was planted in pots of soil soaked with solutions mimicking acid rain, with control and experimental groups receiving neutralizing agents (ammonium hydroxide) or not. Plant growth was measured by analyzing the height of the plants. Results demonstrated that neutralizing agents were effective in improving soil pH levels and that the resulting salts produced were beneficial to the growth of the grass. The findings suggest that this method could be applied on a larger agricultural scale to reduce the harmful effects of acid rain and increase agricultural efficiency.

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.

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.

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.

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, the authors conduct a survey to evaluate the impact of household socioeconomic status on effectiveness of distance learning for students.