Browse Articles

In this study, the authors conduct a survey to evaluate the impact of household socioeconomic status on effectiveness of distance learning for students.

In order to determine whether unmarked soybeans in the market were genetically modified crops, the authors developed a polymerase chain reaction (PCR) screen for DNA lectin.

In this study, the authors develop a new hydrogel using photochemical crosslinking with bovine serum albumin and methylene blue. They find that this new hydrogel has some useful applications!

The authors investigated the relationship between personality traits and adolescent materialism, as well as how materialism relates to spending habits. Results indicate that extroversion was positively correlated with materialism, and that adolescents' purchases were affected by the purchasing behaviors of their friends or peers. Moreover, materialistic youth were more likely than non-materialistic youth to spend money on themselves when given a hypothetical windfall of $500.

The study investigates the antibacterial properties of household spices on bacteria isolated from everyday objects, aiming to address the limited understanding of bacterial resilience on surfaces and the potential of spices as antibacterial agents. Researchers hypothesized that bacteria would grow faster on some surfaces than others and that spices like honey, chili powder, turmeric, and sumac would inhibit bacterial growth at varying rates. The findings suggest that household spices possess significant antibacterial properties and could be used as emergency disinfectants, particularly in under-resourced settings. However, they cannot replace medical treatments but offer insights into alternative health solutions using common ingredients.

This manuscript explores the performance of five different machine learning models in classifying brain tumors from a dataset of MRI scans. The authors find that several of the models showed >90% accuracy. Thus, the authors suggest that machine learning models demonstrate potential for effective implementation in clinical settings, including as a diagnostic tool that can be used to complement the expertise of neuroradiologists.

This study investigates how the hyperparameters epochs and batch size affect the classification accuracy of a convolutional neural network (CNN) trained on pulsar candidate data. Our results reveal that accuracy improves with increasing number of epochs and smaller batch sizes, suggesting that with optimized hyperparameters, high accuracy may be achievable with minimal training. These findings offer insights that could help create more efficient machine learning classification models for pulsar signal detection, with the potential of accelerating pulsar discovery and advancing astrophysical research.

The authors assess a genetic variant within a well-known interaction partner of huntingtin that has been linked to modifying the age of onset of Huntington's disease.

The authors compare current machine learning algorithms with a new Explainable AI algorithm that produces a human-comprehensible decision tree alongside predictions.

The authors looked at the ability to use graphite from pencils in anodes of lithium-anode batteries.