In this study the authors looked at the extract of Stephania tetrandra and its impact on symptoms related to obsessive compulsive disorder in fruit flies.
Read More...Administration of Stephania tetrandra to Drosophila melanogaster to create obsessive compulsive disorder model
In this study the authors looked at the extract of Stephania tetrandra and its impact on symptoms related to obsessive compulsive disorder in fruit flies.
Read More...Solving the Schrödinger equation computationally using the Lanczos algorithm
The authors use the Lanczos algorithm to computationally solve the Schrodinger equation for 2D potentials with a Python program
Read More...Effects of different synthetic training data on real test data for semantic segmentation
Semantic segmentation - labelling each pixel in an image to a specific class- models require large amounts of manually labeled and collected data to train.
Read More...Modeling the heart’s reaction to narrow blood vessels
Cardiovascular diseases are the largest cause of death globally, making it a critical area of focus. The circulatory system is required to make the heart function. One component of this system is blood vessels, which is the focus of our study. Our work aims to demonstrate the numeric relationship between a blood vessel's diameter and the number of pumps needed to transport blood.
Read More...Testing antimicrobial properties of common household spices in a real-world scenario
In this article the authors look at the ability of spices to reduce microbial load on a cutting surface by comparing growth of bacteria cultured before and after cleaning with various spice mixtures.
Read More...Rhythmic lyrics translation: Customizing a pre-trained language model using stacked fine-tuning
Neural machine translation (NMT) is a software that uses neural network techniques to translate text from one language to another. However, one of the most famous NMT models—Google Translate—failed to give an accurate English translation of a famous Korean nursery rhyme, "Airplane" (비행기). The authors fine-tuned a pre-trained model first with a dataset from the lyrics domain, and then with a smaller dataset containing the rhythmical properties, to teach the model to translate rhythmically accurate lyrics. This stacked fine-tuning method resulted in an NMT model that could maintain the rhythmical characteristics of lyrics during translation while single fine-tuned models failed to do so.
Read More...A comparative analysis of synthetic and natural fabrics
The authors test the durability of synthetic versus non-synthetic fabrics though loose thread counts, color fade over time, and shrinkage tests.
Read More...Household spices and minerals as alternative disinfectants for mobile phones
In this study, the authors investigate the disinfectant potential of many household spices and minerals. More specifically, they test whether these compounds can be used to disinfect mobile phones after daily use with the hope of identifying environmentally-friendly cleaning options.
Read More...The effect of nicotine and lead on neuron morphology, function, and ɑ-Synuclein levels in a C. elegans model
E-cigarettes are often considered a healthier alternative to traditional cigarettes. This team of high school authors investigated the impact of common e-cigarette compounds on C. elegans, and found a number of harmful effects ultimately resulting in injury and neuronal damage.
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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