Studying cut-resistant socks for hockey players
Read More...A systematic study of cut-resistant socks for hockey players
Predicting sickle cell vaso-occlusion by microscopic imaging and modeling
The authors use blood smears from individuals with sickle cell disease to correlate sickle cell frequency with the occurrence of vaso-occlusive crises.
Read More...Investigating toxicity and antimicrobial properties of silver nanoparticles in Escherichia coli and Drosophila melanogaster
This paper looks at the antibacterial and toxic effects of silver nanoparticles (AgNPs) on Escherichia coli bacteria and Drosophila melanogaster fruit flies. They modified the AgNPs size, concentration, and surface coating to determine the effects on each of the organisms. For both organisms, increased AgNP concentration demonstrated increased toxicity but particle size and surface coating had opposing effects.
Read More...Drought prediction in the Midwestern United States using deep learning
The authors studied the ability of deep learning models to predict droughts in the midwestern United States.
Read More...Quantum-inspired neural networks enhance stock prediction accuracy
The authors developed a quantum inspired model for stock market fluctuations.
Read More...A comparison of starches and plasticizers for biopolymer synthesis and degradation
Population demographic patterns in PFAS-neurological health research
The authors analyzed racial and ethnic representation in studies on PFAS and neurological health outcomes.
Read More...C reactive protein and risk of neurological deficits and disability in patients with acute ischemic stroke
The authors looked at the correlation between C reactive protein levels and neurological deficits in patients who had suffered an ischemic stroke.
Read More...The correlation between the phase of the moon and the number of psychiatric patients admitted to the hospital
The authors looked at if there was any correlation between the phase of the moon and admissions for psychiatric concerns.
Read More...Optimizing AI-generated image detection using a Convolutional Neural Network model with Fast Fourier Transform
Recent advances in generative AI have made it increasingly hard to distinguish real images from AI-generated ones. Traditional detection models using CNNs or U-net architectures lack precision because they overlook key spatial and frequency domain details. This study introduced a hybrid model combining Convolutional Neural Networks (CNN) with Fast Fourier Transform (FFT) to better capture subtle edge and texture patterns.
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