The authors test the effect of caffeine on the behavior of the Siamese fighting fish Betta splendens.
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Predicting baseball pitcher efficacy using physical pitch characteristics
Here, the authors sought to develop a new metric to evaluate the efficacy of baseball pitchers using machine learning models. They found that the frequency of balls, was the most predictive feature for their walks/hits allowed per inning (WHIP) metric. While their machine learning models did not identify a defining trait, such as high velocity, spin rate, or types of pitches, they found that consistently pitching within the strike zone resulted in significantly lower WHIPs.
Read More...Evaluating the effectiveness of machine learning models for detecting AI-generated art
The authors investigate how well AI-detection machine learning models can detect real versus AI-generated art across different art styles.
Read More...Surface cleanliness of hydrothermally grown zinc oxide microparticles compared to commercial nanoparticles
The authors test the usefulness of zinc oxide microparticles relative to zinc oxide nanoparticles as antibacterial agents.
Read More...Analysis of Milorganite’s ability to sustain growth of Ocimum basilicum in simulated Martian soil
The authors test whether basil can grow in a simulated Martian soil improved with a waste-based fertilizer called Milorganite.
Read More...The effect of Omega-3 on bovine blood cells as a potential remedy for Cerebral Cavernous Malformations
Here, the authors investigated if dietary Omega-3 fatty acids could reduce the potential for cerebral cavernous malformations, which are brain lesions that occur due to a genetic mutation where high membrane permeability occurs between endothelial cell junctions. In a bovine-based study where some cows were fed an Omega-3 diet, the authors found the membranes of bovine blood cells increased in thickness with Omega-3 supplementation. As a result, they suggest that dietary Omega-3 could be considered as a possible preventative measure for cerebral cavernous malformations.
Read More...The effect of activation function choice on the performance of convolutional neural networks
With the advance of technology, artificial intelligence (AI) is now applied widely in society. In the study of AI, machine learning (ML) is a subfield in which a machine learns to be better at performing certain tasks through experience. This work focuses on the convolutional neural network (CNN), a framework of ML, applied to an image classification task. Specifically, we analyzed the performance of the CNN as the type of neural activation function changes.
Read More...Influenza vaccine effectiveness by age for Influenza A/B viruses between 2011-2020
In this manuscript the authors looked at current vaccine strategies against different strains of influenza. Looking at several factors they found that influenza strain as well as vaccinated age group, among other factors, impact vaccine effectiveness.
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...Impact of NaCl concentration in crystalline nanocellulose for printed ionic dielectrics
The authors looked at how the addition of NaCl to crystalline nanocellulose capacitors could improve performance in transistor applications. They found that NaCl can improve performance, but that further work is needed to determine the optimal concentration used depending on the intended application.
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