The authors examine a relationship between tweet sentiment and stock market behavior during the early weeks of the COVID-19 pandemic.
Read More...Understanding investors behaviors during the COVID-19 outbreak using Twitter sentiment analysis
The authors examine a relationship between tweet sentiment and stock market behavior during the early weeks of the COVID-19 pandemic.
Read More...The impact of genetic analysis on the early detection of colorectal cancer
Although the 5-year survival rate for colorectal cancer is below 10%, it increases to greater than 90% if it is diagnosed early. We hypothesized from our research that analyzing non-synonymous single nucleotide variants (SNVs) in a patient's exome sequence would be an indicator for high genetic risk of developing colorectal cancer.
Read More...Pediatric probiotic culture survival study in acidic pH using an in vitro model
In this study, the authors investigate the effects of acidity on the survival of commercial probiotic Lovebug bacterial strains.
Read More...An Experiment to Assess the Usefulness of a Virtual Environment as a Method of Public Speaking Anxiety Exposure
This experiment assessed the effectiveness of a virtual environment as a method of exposure in the treatment of high school students’ public speaking anxiety. The results show that participants’ heartbeat was higher when they wore a VR headset than when they did not.
Read More...Association of depression and suicidal ideation among adults with the use of H2 antagonists
In this study, the authors investigate associations between use of histamine H2 receptor antagonists and suicidal ideation in different age groups.
Read More...Associations between substance misuse, social factors, depression, and anxiety among college students
Here, the authors considered the effects of relationship status and substance use on the mental health of colleges students, where they specifically examined their correlation with depression, anxiety, and the fear of missing out (FoMO). Through a survey of college students they found that those with higher substance misuse had higher levels of anxiety, depression, and FoMO, while those involved in longer-term relationships had lower levels of FoMo and alcohol use.
Read More...Focusing Sound Waves Using a Two-Dimensional Non-Linear System
Sound waves can be amazingly powerful, especially when they work together. Here the authors create an “acoustic lens” that focuses sound waves on a single location. This makes the sound waves very powerful, capable of causing damage at a precise point. In the future, acoustic lenses like this could potentially be used to treat cancer by killing small tumors without surgery.
Read More...Using economic indicators to create an empirical model of inflation
Here, seeking to understand the correlation of 50 of the most important economic indicators with inflation, the authors used a rolling linear regression to identify indicators with the most significant correlation with the Month over Month Consumer Price Index Seasonally Adjusted (CPI). Ultimately the concluded that the average gasoline price, U.S. import price index, and 5-year market expected inflation had the most significant correlation with the CPI.
Read More...Capturing Harmful Air Pollutants Using an Electrospun Mesh Embedded with Zinc-based Nanocrystals
Zeolithic imidazolate framework-8 (ZIF-8) is a specific metal-organic framework that has favorable qualities for use in an air filter and is known to be capable of adsorbing particulate matter. Therefore, the objective of this experiment was to determine the effectiveness of ZIF-8 in adsorbing polar, gaseous air pollutants, specifically nitrogen dioxide and hydrogen sulfide. In order to determine effectiveness, the percent change in concentration for various gases after the application of ZIF-8 crystals was measured via Fourier-transform infrared spectroscopy (FTIR). The work highlights crystals as a potentially promising alternative or addition to current filter materials to reduce atmospheric pollution.
Read More...Prediction of diabetes using supervised classification
The authors develop and test a machine learning algorithm for predicting diabetes diagnoses.
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