The authors develop and test a machine learning algorithm for predicting diabetes diagnoses.
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Contribution of environmental factors to genetic variation in the Pacific white-sided dolphin
Here the authors sought to understand the effects of different variables that may be tied to pollution and climate change on genetic variation of Pacific white-sided dolphins, a species that is currently threatened by water pollution. Based on environmental data collected alongside a genetic distance matrix, they found that ocean currents had the most significant impact on the genetic diversity of Pacific white-sided dolphins along the Japanese coast.
Read More...Are Asian foods healthier than Western foods: Evidence collected from St. Louis area grocery stores
The authors compare nutritional content of foods found in Western versus Asian grocery stores to determine whether one cultural diet is healthier than the other.
Read More...Artificial intelligence assisted violin performance learning
In this study the authors looked at the ability of artificial intelligence to detect tempo, rhythm, and intonation of a piece played on violin. Technology such as this would allow for students to practice and get feedback without the need of a teacher.
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...Evaluating TensorFlow image classification in classifying proton collision images for particle colliders
In this study the authors looked at developing a more efficient particle collision classification method with the goal of being able to more efficiently analyze particle trajectories from large-scale particle collisions without loss of accuracy.
Read More...An explainable model for content moderation
The authors looked at the ability of machine learning algorithms to interpret language given their increasing use in moderating content on social media. Using an explainable model they were able to achieve 81% accuracy in detecting fake vs. real news based on language of posts alone.
Read More...Cleaning up the world’s oceans with underwater laser imaging
Here recognizing the growing amount of plastic waste in the oceans, the authors sought to develop and test laser imaging for the identification of waste in water. They found that while possible, limitations such as increasing depth and water turbidity result in increasing blurriness in laser images. While their image processing methods were somewhat insufficient they identified recent methods to use deep learning-based techniques as a potential avenue to viability for this method.
Read More...Statistical evaluation of the effects of surface processing on aerospace fastener tested strength
In the aerospace industry, various surface processing or coatings are widely used. However, no detailed research on the aerospace fastener tensile and double shear strength variation due to surface processing has been conducted. Thus, the purpose of this study was to systematically evaluate the effect of surface processing on the standard aerospace fastener's tensile and shear properties.
Read More...Exponential regression analysis of the Canadian Zero Emission Vehicle market’s effects on climate emissions in 2030
Here, the authors explored how the sale and use of electric vehicles could reduce emissions from the transport industry in Canada. By fitting the sale of total of electric vehicles with an exponential model, the authors predicted the number of electric vehicle sales through 2030 and related that to the average emission for such vehicles. Ultimately, they found that the sale and use of electric vehicles alone would likely not meet the 45% reduction in emissions from the transport industry suggested by the Canadian government
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