
In this study, the authors survey middle and high school students in different states in the U.S. to evaluate stress levels, learning experiences, and activity levels during the COVID-19 pandemic.
Read More...Exercise, grades, stress, and learning experiences during remote learning due to the COVID-19 pandemic
In this study, the authors survey middle and high school students in different states in the U.S. to evaluate stress levels, learning experiences, and activity levels during the COVID-19 pandemic.
Read More...Machine Learning Algorithm Using Logistic Regression and an Artificial Neural Network (ANN) for Early Stage Detection of Parkinson’s Disease
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).
Read More...Correlation of Prominent Intelligence Type & Coworker Relations
Ashley Moulton & Joseph Rasmus investigate 9 controversial categories of intelligence as predicted by Multiple Intelligence Theory, originally proposed in the mid-1980s. By collecting data from 56 participants, they record that there may not actually be a correlation between these categorical types when it comes to workplace atmosphere and project efficiency.
Read More...The role of furry friends in facilitating social interaction during the COVID-19 pandemic
The COVID-19 pandemic has caused disruption in social interactions. In this study, the authors tested if walking a dog will change human interactions and found that walking with a dog increased social interaction.
Read More...The relationship between digit ratio and personality: 4D:5D digit ratio, sex, and the trait of conscientiousness
In this study, the authors use quantitative digit ratio measurements and a survey of personality traits to evaluate the potential relationship between sex and levels of conscientiousness.
Read More...Comparison of Perception of 2020 Election Security Threats Between Young and Old Voters
In this study, results from an extensive survey report college students' and senior citizens' voting concerns during the 2020 presidential election.
Read More...Two Wrongs Could Make a Right: Food Waste Compost Accelerated Polystyrene Consumption of Tenebrio molitor
Expanded polystyrene (EPS) is a plastic used to make food containers and packing materials that poses a threat to the environment. Mealworms can degrade EPS, but at a slow rate. Here, researchers assessed the impact of food waste compost and oats on the speed of EPS consumption by mealworms, superworms, and waxworms. A positive correlation was found between food waste compost supplementation and EPS consumption, especially by mealworms, indicating a potential industrial application.
Read More...A Study on the Coagulating Properties of the M. oleifera Seed
In this study, the authors investigate whether Moringa Oleifera seeds can serve as material to aid in purifying water. M. oleifera seeds have coagulating properties and the authors hypothesized that including it in a water filtration system would reduce particles, specifically bacteria, in water. Their results show that this system removed the largest percent of bacteria. When used in combination with cilantro, it was actually more efficient than the other techniques! These findings have important implications for creating better and more economical water purification systems.
Read More...Correlations between Gray-White Matter Contrast in Prefrontal Lobe Regions and Cognitive Set-Shifting in Healthy Adults
This study uses neuroimaging to investigate cognitive set-shifting, a type of executive function that involves shifting from one task to another. This study tested whether cortical gray-white matter contrast in subregions of the prefrontal cortex (PFC) was associated with set-shifting abilities in adults.
Read More...Differential privacy in machine learning for traffic forecasting
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.
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