The authors survey adolescents about aspects of the COVID-19 pandemic to explore perspectives that may give rise to cognitive dissonance.
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Identifying factors, such as low sleep quality, that predict suicidal thoughts using machine learning
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.
Read More...Effects of cleaning agents on bacterial growth on refrigerator surfaces
Comparing the performance of lateral control algorithms on long rigid vehicles in urban environments
Here, seeking to better understand the control algorithms used in autonomous vehicles, the authors compared the Stanley and pure pursuit control algorithms along with a new version of each. Unexpectedly, they found that no control algorithm offered optimal performance, but rather resulted in tradeoffs between the various ideal results.
Read More...Analysis of ultraviolet light as a bactericide of gram-negative bacteria in Cladophora macroalgae extracts
Here, the authors sought to use Cladophora macroalgae as a possible antibiotic to address the growing threat of antibiotic resistance in pathogenic bacteria. However, when they observed algae extracts to be greatly contaminated with gram-negative bacteria, they adapted to explore the ability to use ultraviolet light as a bactericide. They found that treatment with ultraviolet light had a significant effect.
Read More...Biofortification of Raphanus sativus through irrigation with Ca2+ solutions does not increase calcium content
This study is centered around developing biofortification methods: the authors test whether the amount of calcium available to growing crops translates into more calcium present in the crops.
Read More...Prediction of molecular energy using Coulomb matrix and Graph Neural Network
With molecular energy being an integral element to the study of molecules and molecular interactions, computational methods to determine molecular energy are used for the preservation of time and resources. However, these computational methods have high demand for computer resources, limiting their widespread feasibility. The authors of this study employed machine learning to address this disadvantage, utilizing neural networks trained on different representations of molecules to predict molecular properties without the requirement of computationally-intensive processing. In their findings, the authors determined the Feedforward Neural Network, trained by two separate models, as capable of predicting molecular energy with limited prediction error.
Read More...Reducing levels of C-Reactive Protein: An eight-week, open-label clinical trial of three oral supplements
In this study, the effects of vitamin C, ginger, or curcumin supplements on C-reactive protein levels in healthy participants are determined in an eight-week open-label trial.
Read More...Misconceptions regarding heart disease are prevalent among american adults and minors
In this study, the authors created a survey to assess misconceptions and knowledge deficits regarding cardiovascular diseases exist among US adults and minors.
Read More...Fourier-Transform Infrared (FTIR) spectroscopy analysis of seven wisconsin biosolids
The authors analyzed biosolids from five Wisconsin wastewater treatment plants and suggest using KBr pellet FTIR as a simple and rapid method to start characterizing P species in biosolids.
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